Juan Pedro Berro Ramirez

Damien Halm
email: damien.halm@ensma.fr

Jean -Claude Grandidier

ASSESSMENT OF A DAMAGE MODEL FOR WOUND COMPOSITE STRUCTURES BY ACOUSTIC EMISSION

Keywords: Damage Model, Acoustic Emission, Finite Element Analysis, Wound Composite

This paper deals with the link between the acoustic activity due to damage in wound composite structures and the simulated evolution of damage variables. A damage model able to accurately capture the different degradation modes which may occur in this type of material is first selected. A parallel is drawn between the continuous evolution of the damage variables and the discrete acoustic events. The mechanical behavior of notched samples made of two different wound composite lay-ups and involving complex damage combinations are simulated and compared to acoustic emission. The satisfactory correlation confirms the physical meaning of the damage variables and validates the model assumptions. The association of experimental acoustic signals and a damage model is a tool to understand the degradation scenario of wound composite structures and the meaning of the recorded acoustic events.

2 mechanical properties' degradation and potentially final failure of the structure. Several approaches attempt to predict failure of composite structures by using fracture criteria ( [START_REF] Hashin | Failure criteria for uni-directional fibre composites[END_REF][START_REF] Tsai | A general theory of strength for anisotropic materials[END_REF] among many others). Xu et al [START_REF] Xu | Finite element analysis of burst pressure of composite hydrogen storage vessels[END_REF] compare the capability of these simple criteria to predict composite pressure vessel burst. These approaches, in their initial version, provide only binary indications (failed / not failed) and do not capture the progressive nature of these phenomena. One alternative to simulate any material damage process, and in particular its progressiveness, is the Continuun Damage Mechanics. For several years, a large number of damage models have been developed for multiple applications, from the ductile materials to quasi -brittle behaviour. The framework of Continuum Damage Mechanics has considerably evolved since the pioneering works of Kachanov [START_REF] Kachanov | On the time to failure under creep conditions[END_REF] . This formalism has proved its advantages in different applications, as it can be seen in the work by Chaboche [START_REF] Chaboche | Continuum Damage Mechanics, parts I and II[END_REF], Krajcinovic [6] or Talreja et al. [START_REF] Talreja | Transverse cracking and stiffness reduction in cross ply laminates of different matrix toughness[END_REF] among many others.

Numerous models written in this framework are dedicated to composite materials (specifically CFRP). For example, some models focus on microscopic scale. These models study the different material constituents like fibers, matrix, interfaces [START_REF] Blassiau | Modelisation des phenomenes microstructuraux au sein d'un composite unidirectionnel carbone/epoxy et prediction de durée de vie: controle et qualification de reservoirs bobinés[END_REF][START_REF] Haj-Ali | Nested nonlinear micromechanical and structural models for the analysis of thick-section composite materials and structures[END_REF].

Other models focus on the "mesoscale", namely the ply scale [START_REF] Lubineau | An enhanced mesomodel for laminates based on micromechanics[END_REF][START_REF] Abisset | On the validation of a damage mesomodel for laminated composites by means of open-hole tensile tests on quasi-isotropic laminates[END_REF][START_REF] Benamira | Behaviour to failure of fibre mat reinforced composite under combined loading conditions[END_REF][START_REF] Hochard | A generalized damage model for woven ply laminates under static and fatigue loading conditions[END_REF][START_REF] Ladevèze | A mesomodel for localisation and damage computation in laminates[END_REF][START_REF] Thollon | A general damage model for woven fabric composite laminates up to first failure[END_REF][START_REF] Laurin | A multiscale progressive failure approach for composite laminates based on thermodynamical viscoelastic and damage models[END_REF]. This class of models proved to provide excellent results in the WWFE (World Wide Failure Exercise, [START_REF] Soden | A comparison of the predictive capabilities of current failure theories for composite laminates[END_REF]18]).

In general, the damage evolution law is identified and validated by comparison of experimental mechanical response and damage observations with simulated ones. As the loss of stiffness is the main mechanical signature of damage, the measure of the change in elasticity is commonly used to identify the evolution laws of the variables [19]. Another technique which is more and more used to study damage in composite materials is the Acoustic Emission monitoring. Acoustic waves are generated in solids by the rapid release of strain energy [START_REF] Eitzen | Acoustic emission: Establishing the fundamentals[END_REF]. For example, a crack growth releases a given quantity of elastic energy. AE monitoring is the detection of these elastic waves due to damage growth. The high sensitivity of AE testing, coupled with its need for relatively few sensors, make it an attractive technique. It has been extensively used and assessed as an alternative damage measurement technique [START_REF] St Kim | Characteristics of damage and fracture process of carbon fiber reinforced plastic under loading-unloading test by using AE method[END_REF][START_REF] Ceysson | Damage mechanisms characterisation of carbo fibre / epoxy composite laminates by both electrical resistance measurements and acoustic emission analysis[END_REF][START_REF] Ds De Vasconcellos | Tension-tension fatigue behaviour of woven hemp fibre reinforced epoxy composite: A multi-instrumented damage analysis[END_REF]. One sensor allows one recording the acoustic signals. With at least two sensors, it is possible to locate the source of the echoes [START_REF] Scholey | Quantitative experimental measurements of matrix cracking and delamination using acoustic emission[END_REF][START_REF] Se Mechraoui | Reliability of damage mechanism localisation by acoustic emission on glass/epoxy composite material plate[END_REF]. This is an advantage for large composite structures, such as wind turbine blades [START_REF] Yang | Progress and trends in nondestructive testing and evaluation for wind turbine composite blade[END_REF][START_REF] Tang | An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades[END_REF]: in spite of the scale difference (defects of a few centimetres in a blade whose length is several tens of meter), AE monitoring can successfully provide early warning of damage onset and propagation. It also makes possible to distinguish between the damage modes by the use of cluster analysis together with classification techniques [START_REF] Godin | Clustering of acoustic emission signals collected during tensile tests on unidirectional glass/polyester composite using supervised and unsupervised classifiers[END_REF][START_REF] Godin | Integration of the Kohonen's self-organising map andkmeans algorithm for the segmentation of the AE data collected during tensile tests on cross-ply composites[END_REF][START_REF] Godin | Influence of hydrolytic ageing on the acoustic emission signatures of damage mechanisms occurring during tensile tests on a polyester composite: Application of a Kohonen's map[END_REF].

The acoustic emission is directly related to the damage phenomena occurring in the composite material. In the view of determining the mechanical strength of filamentwound pressure vessels, Mahdavi et al. [START_REF] Hr Mahdavi | Failure analysis of (±55°) filament-wound pipes using acoustic emission technique[END_REF] proved the acoustic parameters (energy, accumulated energy, amplitude,…) can be used to describe the failure mechanisms in ring specimens subjected to hoop tensile stress. Blassiau et al. [START_REF] Blassiau | Micromechanisms of load transfer in a unidirectional carbon fibre-reinforced epoxy composite due to fibre failures: Part 3. Multiscale reconstruction of composite behaviour[END_REF] compared the number of accumulated acoustic events with model predictions of fibre breakage in a unidirectional composite and proposed a tool to assess the lifetime of hydrogen vessels subjected to constant inner pressure. These works show the direct link between damage and acoustic activity in complex composite structures such as wound tanks. The objective of this work is to correlate AE to the evolution of internal variables in order to reinforce their physical meaning. The damage model selected here has been proposed by Berro Ramirez et al. [START_REF] Berro Ramirez | A fixed directions damage model for composite materials dedicated to hyperbaric type IV hydrogen storage vessel -Part I: model formulation and identification[END_REF] and is dedicated to the simulation of degradation mechanisms observed in wound composite pressure vessels. It assumes the failure is the result of the combination of elementary damage modes quantified by internal variables and whose effects are assembled in the expression of the constitutive relation through the tensorial functions representation theory. Showing the simulated damage evolution matches the acoustic events is a mean of validating the model assumptions and of proving that it is capable of simulating the behavior of complex composite structures.

Such equivalences have been addressed in the literature but in general with simpler models that do not distinguish the different modes of damage [START_REF] Lemascon | Contrôle des matériaux composites par émission acoustique[END_REF].

This work highlights the parallel between the damage variables of a Continuum

Damage Mechanics (CDM) model (which are the outputs of a Finite Element analysis and are considered as "virtual" acoustic echoes) and the measured acoustic energy. This comparison needs two tools:

-a damage model, which has to simulate accurately all the damage modes undergone by wound composite structures and their consequences on the stiffness. Section 2 briefly recalls the outlines of the model developed by Berro Ramirez et al. [START_REF] Berro Ramirez | A fixed directions damage model for composite materials dedicated to hyperbaric type IV hydrogen storage vessel -Part I: model formulation and identification[END_REF] for wound composite materials. It is based on the concepts of CDM and has been used to simulate the behavior of notched structures and finally applied on pressure vessel burst simulations [START_REF] Berro Ramirez | 700 bar type IV high pressure hydrogen storage vessel burst -simulation and experimental validation[END_REF].

-a procedure to link damage evolution (a continuous process) and acoustic emission (discrete events) from tests involving only one damage mode. Section 3 specifies the "virtual" acoustic emission procedure, its implementation and identification. Section 4 presents the comparison between the simulated acoustic signals and the experimental results obtained from tensile tests carried out on two types of specimens ([±45 4 ] s and a quasi-isotropic sequence [0 2 , ±45 2 ,90 2 ] s ) representative of the structure of wound composite vessels and involving complex spatial and temporal combinations of damage modes. This comparison aims to validate the model's ability to simulate damage and its consequences accurately not only at the macroscopic scale (tensile failure load, elasticity change, see Berro et al. [START_REF] Berro Ramirez | 700 bar type IV high pressure hydrogen storage vessel burst -simulation and experimental validation[END_REF]) but also at mesoscopic scale (local damage mechanisms evidenced by acoustic emission).

OUTLINES OF THE DAMAGE MODEL

This section summarizes the main features of the damage model which has been developed to simulate the behavior of wound composite vessels subjected to inner pressure [START_REF] Berro Ramirez | 700 bar type IV high pressure hydrogen storage vessel burst -simulation and experimental validation[END_REF]. A more detailed presentation can be found in [START_REF] Berro Ramirez | A fixed directions damage model for composite materials dedicated to hyperbaric type IV hydrogen storage vessel -Part I: model formulation and identification[END_REF].

• Damage mechanisms

The damage modes activated in the composite shell of wound pressure vessels are of two types:

(i) brittle fracture (fibre failure, fibre -matrix interface failure and delamination), (ii) progressive degradation due to fibre matrix debonding and matrix microcracking (the so called diffuse damage [START_REF] Lubineau | An enhanced mesomodel for laminates based on micromechanics[END_REF]).

Two additional phenomena are incorporated in order to get a better representation of the composite behaviour when subjected to shear loading, namely matrix viscosity and permanent shear strain.

• Fixed directions approach -internal variables

The model is built in the framework of the Continuum Damage Mechanics using the concepts and tools of the Thermodynamics of Irreversible Processes. The model has to simulate the mechanical behaviour of composite structures undergoing the following phenomena:

-non linear degradation of the stiffness due to the various damage mechanisms -initial and damage induced anisotropy -permanent shear strain and viscosity

To build the model, the fixed damage directions concept based on the work by Boelher [START_REF] Boehler | Anisotropic constitutive equations for continous media[END_REF] is used. According to [START_REF] Bargellini | Modelling of anisotropic damage by microcracks: towards a discrete approach[END_REF], it is supposed that every damage state (anisotropic by nature) is decomposed, at the meso -scale (i.e., at the ply scale), in a set of couples , , whose number is to be defined. The fixed directional tensors are defined as = ⨂ , where is the normal to the fracture surface and is related to the aforementioned damage modes. These directions are chosen according to the material anisotropy. Damage growth is represented by the evolution of the scalar damage internal variables associated to the corresponding damage system. These damage variables evolve from 0 (for a sound material) to 1 (maximum damage).

• Choice of the damage systems At the ply scale, the vectors allowing to build the directional tensors i N associated to the damage modes are respectively (fibre direction), (normal to the fibre direction), (normal to the ply) and (at 45° of the direction i and j). These vectors are represented in Figure 1, according to [START_REF] Berro Ramirez | A fixed directions damage model for composite materials dedicated to hyperbaric type IV hydrogen storage vessel -Part I: model formulation and identification[END_REF].

Figure 1: Damage systems at the ply scale From these vectors, four sets of directional tensors are considered:

• N related to fibre direction and fibre fracture. The scalar variable associated is ρ

• N related to transverse direction and associated to fibre -matrix interfaces fracture in tension. The corresponding variable is ρ .

• N related to the direction normal to the ply plane and associated to delamination phenomena (Mode I). Its scalar damage variable is ρ .

• N (i and j take the values 0, 90 and HP:

N , N , N , N , N ,

• Thermodynamic potential

The tensorial functions representation theory [START_REF] Boehler | Anisotropic constitutive equations for continous media[END_REF] is a guide to build the thermodynamic potential (strain energy per unit volume, noted w in the following) from tensorial invariants. At the ply scale, three parts can be distinguished: an elastic contribution w %&' ( ) , a damage term w *'+',-involving each damage variable ρ i and the viscous contribution w . )/0 .

1 23 4 , , , , 5 6 7
8 , " ̿ , 9 :; = (1) where E the elastic strain tensor, which is the difference between the total strain J̿ and the permanent strain tensor Ψ defined as:

1 <=>?@ A 3 4 , + 1 C>D>EF 23 4 , , , , 5 6 7 8
K = L 4 -M = L 4 -NO P Q RQS -P Q RQT + O P Q RQT -P Q RQS U (2)
In Equation ( 1), 5 6 7

8 and 9 : stand for the set of shear directional tensors and the set of damage variables, respectively. As the initial ply is transversely isotropic, the corresponding elastic potential is built using only the directional tensor 0 N [START_REF] Halm | A modular damage model for quasi-brittle solidsinteraction between initial and induced anisotropy[END_REF]. The detailed expressions of w elastic , w Damage and w Viscous , built from the theory of tensorial functions representation, can be found in [START_REF] Berro Ramirez | A fixed directions damage model for composite materials dedicated to hyperbaric type IV hydrogen storage vessel -Part I: model formulation and identification[END_REF].

• Reversibility domains and evolution laws

According to the Thermodynamics of Irreversible Process, the elastic stress V 4 and the thermodynamic forces W X Y , W Z [ , W Z \ and W `Y\a associated to each internal variable (ρ i , γ + , γ -and z i-j respectively) are obtained by deriving w:

V 4 = bc b< 4 W X Y = - bc bX Y W Z [ = - bc bZ [ W Z \ = - bc bZ \ W `Y\a = - bc b`Y \a (3)
The evolution of the internal variables is found to follow the normality rule with respect to the elastic domain. The general expression of the reversibility domain associated to each internal variable reads:

d e f g e f , e f ≤ Q, d O g O ± , O ± ≤ Q (4)
According to the damage evolution type, the threshold function f ρi takes different forms [START_REF] Berro Ramirez | A fixed directions damage model for composite materials dedicated to hyperbaric type IV hydrogen storage vessel -Part I: model formulation and identification[END_REF]: it is a linear function of the thermodynamic force is the case of a brittle behaviour, whereas it tends towards an asymptote for a more progressive damage. Regarding the threshold of the shear permanent strains γ + and γ -, a function linear with respect to the corresponding thermodynamic force is selected. The normality rule, associated to the threshold function, is found to be flexible enough to simulate both types of damage evolution (brittle and progressive) and the permanent strain evolution.

Unlike damage, viscosity is assumed not to be limited by a threshold function but to occur as soon as a shear strain is applied, the evolution of the variables z i-j is directly proportional to the corresponding thermodynamic force. The detailed expressions of the threshold functions, as well as that of the thermodynamic potential and the evolution laws can be found in [START_REF] Berro Ramirez | A fixed directions damage model for composite materials dedicated to hyperbaric type IV hydrogen storage vessel -Part I: model formulation and identification[END_REF]. Note that a probabilistic version of this model taking into account the statistical material strength distribution can be found in [START_REF] Berro Ramirez | Deterministic vs. probabilistic burst prediction of wound composite pressure vessel[END_REF]. The variability sources are the random failure of the fiber and the volumetric fraction of fibers. For the sake of simplicity, only the deterministic version is used in this paper.

The model is first implemented in the Finite Element software ABAQUS by the means of a subroutine UMAT, which allows to simulate any mechanical behaviour of a structure by using a specifically designed model. In a second step, this UMAT subroutine is used to identify the model parameters by simulating tensile loadings performed on three different types of specimens made from wound pipes ([0 8 ], [90 8 ],

[±45 4 ] s ). These tubes are first manufactured by filament winding on a cylindrical mandrel. Two types of plies are manufactured: circumferential plies used for layers oriented at 0 ° and 90 ° (with no interlacing) and helical plies with interlaced fibers. The tubes are then cut along the shaft and laid down flat. The plates the samples are taken from are made by stacking these elementary wound plies, after which they are placed in a vacuum bag and cured in an autoclave (see [START_REF] Berro Ramirez | Experimental study of thermo mechanical behavior of wound notched structures[END_REF] for details about manufacturing of these samples).

"VIRTUAL" ACOUSTIC EMISSION

The acoustic emission, whose principles have been briefly recalled in Section 1, allows one getting a qualitative assessment of the damage onset and its location in the studied structure. It is also a way to validate and emphasize the capabilities of the damage model presented in the previous section by associating virtual echoes (i.e., simulated damage evolution) to each damage event experimentally recorded. A parallel is drawn between these virtual echoes and the acoustic energy measured by sensors during tensile tests performed on flat specimens.

These virtual echoes are "listened" (actually, simulated) by using a specific type of variable in an ABAQUS UMAT subroutine. Three different echoes are used to simulate the global acoustic signal. These echoes are associated to (i) fiber breakage (damage variable ), (ii) matrix / fiber interface fracture (damage variable ) and (iii) diffuse damage (damage variable ? ). Note that out-of-plane damage is ignored: the tensile test performed on flat samples will be simulated in a 2D framework (samples are meshed with shell elements). However, echoes related to out-of-plane damage could be easily introduced in the numerical procedure.

In the framework of a CDM model, the material stiffness is weakened by damage variables whose evolution is supposed to represent the succession of degradation events.

These variables progressively increase (more or less rapidly) and, in the same way, the acoustic events (each characterized by a given energy level) appear from a low strain level and are cumulative until final fracture. The key aspect of this work is to link simulated damage evolution and acoustic events: it is necessary to assign an energy These two curves clearly exhibit the predominance of low and intermediate energy echoes which can be associated to diffuse and fibre / matrix interface damage, as pointed out on a similar material in Bertin [START_REF] Bertin | Fatigue thermomécanique de multicouches polymère/composite[END_REF]. Fibre breakage is characterized by much more energetic events, which can be observed at the right side of Figure 3a, at the very end of the test. Consequently, it appears reasonable to choose three energy levels: a low energy level, associated to diffuse damage (points at the bottom of Figure 3b), an intermediate one, associated to fibre / matrix interface damage (visible in particular between 1000s and 1200s), and a high one corresponding to the fibre breakage.

Once the number of energy levels has been chosen, it is necessary to link a discrete phenomenon (the acoustic signal) to a continuous one (the damage variable evolution for each damage mode). A possible way to make this association is to emit one virtual echo when the damage variable reaches the maximum value = 1 (or a lower value).

However, this method is not very realistic because some phenomena (such as fibre / matrix debonding, diffuse matrix cracking, breakage of isolated fibres) are progressive by nature and can generate several echoes before the damage variable has reached its maximum value. The method chosen here is based on a pseudo -discrete acoustic emission, which is a compromise between a discrete event and a continuous process. A set of n arbitrary damage values (ρ i-1 , ρ i-2 ,…, ρ i-n ) is selected and at each value the corresponding simulated strain level (J j X Y , J k X Y ,…,J 7 X Y ) is recorded (see in Figure 4 a schematisation of a damage variable evolution curve). An energy level S 1 , S 2 ,…, S n is assumed to be released as soon as strain reaches the aforementioned values. This principle is illustrated in Figure 4. The cumulated energy (acoustic energy cumulated over the time), which is one of the standard post-processing coming from experimental data, is thus used in this study. From the FE point of view, the energy level corresponding to each numerical acoustic energy release is stored and cumulated in a history variable, which means that for each time step the energy released by all integration points is added to the former value. This way to store the acoustic energy allows to compare directly with experimental data.

In this case, without loss of generality, three damage levels are chosen for the variable ρ i (i.e., for a given damage mechanism). Consequently, the acoustic energy is assumed to be released in a stepwise manner with three different levels S 1 , S 2 and S 3 .

The values J l X Y and J m X Y stand for the strain at damage onset and at maximum damage, respectively. The choice of the number of damage levels is arbitrary but it must match the damage progressiveness: the more progressive the damage evolution, the higher n.

It is now necessary to define the energy values S 1 , S 2 and S 3 . The starting point is the total energy released by the considered damage mechanism. This energy value is chosen according to the experimental records in Figure 3: in the order of 10 6 aJ for fiber, 10 4 aJ for matrix cracking and 10 2 aJ for diffuse damage. Each intermediate energy level S j (with j=1,2,3) is defined as the total energy of the event type (noted S in Equation 5)

weighted by the corresponding strain range normalized by the total strain range of acoustic activity [J j X Y , J m X Y ]:

n = n o a[p q Y o a q Y o r q Y o p q Y (5)
where the strain values J X Y and J j X Y are the bounds of the j-th energy interval determined from the selected damage levels ρ i-j (Figure 4). For the n-th (last) interval, J 7 j

X Y =J m X Y .
This method is applied to identify the values of the acoustic energy associated with the three damage modes: fiber breakage, fiber / matrix interface failure and diffuse damage.

Acoustic data from two different tests are used in order to identify the different energy levels. It is important to keep in mind the qualitative nature of this approach. These two tests are:

• Tensile test on [± 45 4 ]s samples, characterized by an early initiation of the diffuse damage, followed by a contribution of the fiber / matrix interface failure. The results are presented in Figure 3.

• Tensile test on a sample containing fibers oriented at 0°. Here, data from notched [0 2 ,± 20 2 ] s samples have been selected, which undergo fiber breakage (Figure 5). -fiber / matrix interface failure is associated to only one acoustic level. This choice is justified by the brutal onset and propagation of the kind of damage and seems to be well suited to this type of energy release -the damage levels for fiber breakage are chosen such that energy is released in the last stages -the energy release by diffuse damage is supposed to be more uniformly distributed.

The method presented above is able to simulate the virtual acoustic emission at one material point of the structure: indeed, in a Finite Element approach, the damage evolution (and equivalently the acoustic emission) is calculated at each integration point of the structure. However, as the acoustic sensors in the experiment schematized in Figure 2 record the events that occurred in the whole sample, it is necessary to compare these experimental data with a variable representing the sum of all virtual signals emitted at each integration point. To this end, the UMAT subroutine allows to use a specific variable, called SCD, which has the ability to sum the contributions of all finite elements in a given increment of time. This variable is originally dedicated to the calculation of creep dissipated energy, but its use is here modified so that it represents, at each time integration, the sum, in the whole structure, of the simulated energy echoes energies emitted by fiber breakage, fiber / matrix interface debonding and diffuse damage respectively.

SIMULATION OF THE ACOUSTIC EMISSION IN WOUND COMPOSITE SAMPLES SUBJECTED TO TENSION

The simulation of the acoustic emission in wound composite structures is compared with the experimental energy recorded during tests performed on notched samples. The geometry of these notched wound composite samples is given in Figure 6.

These experimental results come from a test campaign performed on different types of samples [START_REF] Berro Ramirez | Experimental study of thermo mechanical behavior of wound notched structures[END_REF]. Note that although flat these notched samples have been manufactured by filament winding, in the same way as those used to identify the acoustic energy related to the damage evolution (previous section), and then cut by water jet.

Results of tensile test on [±45 4 ] s sample

In order to compare the experimental and simulated acoustic signals, the interferences occurring at the end of the test (acoustic post-failure emissions) have been removed as they do not convey information on the initiation and evolution of damage. In addition, the test duration and the cumulative energy have been normalized in order to compare the shape of the curves. This curve processing eases the comments of the changes in the slopes and allows to draw a parallel between the acoustic energy and the different damage modes. The comparison is shown in Figure 7. -in Part a of the curve, only a small zone of the sample in the vicinity of the notch undergoes diffuse damage (see Figure 8a) and therefore it emits energy signals.

However, this damage evolution (and consequently the simulated acoustic emission) is limited: as specified in Section 2, the choice of the reversibility domain for the diffuse (progressive) damage leads to asymptotic evolution and the damage level cannot exceed a given value.

-when damage in the neighborhood of the notch is close to saturation, diffuse damage starts to spread in the areas of the specimen located on both sides of the notch (Figure 8b). This spatial extension explains the change of slope at the beginning of Part b. Thus, changes in slope of cumulative energy curves may indicate an increase in the size of the damaged area and not the onset of another type of damage.

-at the end of Part b (in the simulated curve), an acceleration of the acoustic energy can be noticed: it corresponds to the onset of fiber / matrix interface debonding and not to diffuse damage growth since this degradation mode has spread throughout the sample and reached its maximum value (Figure 8c). The presence of this acceleration is validated by the occurrence of a jump at the same time on the experimental curve before the sudden and quick increase of energy leading to final failure.

Note that, beyond the identification of the energy levels presented in Section 3, the simulation of the [±45 4 ] s samples is a good way to interpret the different regimes which are observed on the acoustic energy curve and to related them to damage in specific zones.

Figure 8: Simulated diffuse damage level during phases a, b and c of the acoustic emission curve (the darker the color, the higher the damage level)

Results of tensile test on [0 2 , ±45 2 , 90 2 ] s sample

In the same way as for the [±45] samples, the acoustic emission is recorded during tensile tests performed on quasi-isotropic samples (made of the same material and manufactured in a similar way) and compared to the corresponding simulation. Matrix cracking is also found in the 0° plies: this splitting phenomenon is a precursor of fiber breakage, which starts when the slope suddenly increases at a normalized time of about 0.85 (marked by a green circle) and leads to a rapid final failure. The model captures in a satisfactory way the onset of more energetic events: the slope change of the simulated curve exactly coincides with jumps in the experimental records due to the first fiber failure. In the last part of the test, experimental and simulated curves exhibit the same slope and it is worth noting that the model is also able to simulate the energy saturation in the very last step of the test, in accordance with experiment.

CONCLUSION

This paper has investigated a way to link the acoustic activity due to damage in wound composite structures subjected to monotonic loading and the simulated evolution of damage variables. This correlation is made possible thanks to a model able to accurately capture the different damage modes which may occur in this type of material (fiber breakage, diffuse matrix microcracking, fiber / matrix debonding). A specific methodology draws the parallel between the damage variables (whose evolution is by nature continuous) and the discrete acoustic events, by defining a given number of load intervals associated to given energy levels. This approach allows simulating the succession of the different acoustic events (i.e., the emission onset, the transition between different types of events,…) and interpreting the damage kinetics (activation / deactivation of damage modes, spatial extension of damaged areas,…). The damage model parameters are identified only from mechanical tests. Regarding identification, two ways to improve it can be discussed, although the agreement between damage and acoustic activity is satisfactory. The first one consists of inverse characterization through optimization method in order to fit the acoustic signatures of each damage mode. Other laminate sequences could be interesting to perform this kind of identification, like [0,90] s laminates, where matrix transverse cracks can evolve without causing the coupon's catastrophic failure. With such kind of samples, both fibers rupture and transverse cracks can be identified. A second way which may improve the method is to perform a sensitivity (or robustness) study on each acoustic signature.

The good correlation between the evolution of internal variables and acoustic emission confirms the physical meaning of these variables and validates the model assumptions (in particular, damage in this type of composite structures can be decomposed in elementary degradation modes as presented in the expression of the thermodynamic potential whose expression is obtained by the means of the tensorial functions representation theory). On the other hand, the simulation of damage evolution could be a way to identify the acoustic events: Chou et al. [START_REF] Chou | Acoustic emission analysis of composite pressure vessels under constant and cyclic pressure[END_REF] show that acoustic emission is capable of recording accumulation of damage events in composite pressure vessel but cannot distinguish the various damage modes. The association of experimental acoustic signals and a reliable damage model can thus be seen as a complementary tool to understand the degradation scenario of wound composite structures and the meaning of the recorded acoustic events, in addition to optical observations (micrography, Xray,…). The satisfactory correlation between acoustic emission and damage evolution also opens the way to an identification procedure for the damage model parameters based on the use of acoustic activity recorded during tensile tests performed on elementary composite samples.

  N) related to shear directions (+45° (p) and -45° (n)) with respect to i-th direction. These directional tensors are associated to socalled diffuse damage (that is to say, matrix micro -cracking, fibre -matrix debonding, delamination (mode III) in 0° -90° directions and delamination (mode II) in 0°/90° -HP directions) related to shear loadings, permanent shear viscosity. The scalar damage variable associated to diffuse damage is noted ρ in the following.The permanent strain, due to irreversible sliding of cross-plies (visible in particular on [±45] samples) is assumed to be the sum of elementary sliding contributions and which are activated by in-plane shear strains and are associated respectively to the directions N -N and N -N . The global permanent strain tensor Ψ adds the contribution of the elementary sliding variables. Regarding the viscous behaviour (attributable to the matrix and which can be observed under shear), it is represented by a set of scalar variable ! , ! and ! associated to the inplane and out-of-plane shear directions. The global viscous variable " ̿ contains the contribution of each elementary variable ! .

  signature to each modeled damage mode. To this end, tensile tests are performed on parallelepipedic (250mm x 25mm x 2mm) [±45 4 ] s samples cut from wound composite pipes. The material is a prepreg composite composed of Toray T700SC fibres and a TCR UF3369 thermoset epoxy matrix. The acoustic acquisition set-up is composed of two piezo-electric sensors Micro80 (with a silicon grease couplant) and of a PCI-2 system from Mistras Group with a detection threshold of 35dB. The preamplifier gain is 40dB. Figure2displays a schematization of these tests.

Figure 2 :Figure 3

 23 Figure 2: Experimental device: mechanical loading and acquisition of acoustic signals

Figure 4 .

 4 Figure 4. Link between progressive damage evolution / discrete energy release.

Figure 5 :

 5 Figure 5: Acoustic energy vs. time recorded during a tension test on a notched [0 2 ,± 20 2 ] s sample

Figure 6 :

 6 Figure 6: dimensions of the notched samples

Figure 7 .

 7 Figure 7. Comparison experimental and simulated acoustic emission for a [±45 4 ]s

Figure 9 :

 9 Figure 9: Comparison between experimental and simulated acoustic emission for a

Table 1 :

 1 Selected damage values and corresponding acoustic energy valuesOne can observe in this table that:

Time (s) Acoustic Energy Acoustic Energy vs Time

ACKNOWLEDGEMENTS 27 This work was supported by the French National Research Agency (ANR) through Hydrogène et Piles à Combustible (Project OSIRHYS IV, ANR-09-HPAC-010).


Bertrand Massot

Sébastien Arthaud

Baptiste Barrillot

Johanna Roux

Gianina Ungurean

Pierre-Hervé Luppi

Niels C Rattenborg

Paul-Antoine Libourel
email: pa.libourel@univ-lyon1.fr

Pierre- Hervé Luppi

ONEIROS, a new miniature standalone device for recording sleep electrophysiology, physiology, temperatures and behavior in the lab and field

Keywords: Wireless, telemetry, datalogger, electrophysiology, sleep, pigeon, sleep deprivation

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

Sleep is a vital and complex behavioral state that competes with the time allocated to foraging, courtship, parental care, and vigilance [START_REF] Lesku | Adaptive Sleep Loss in Polygynous Pectoral Sandpipers[END_REF][START_REF] Rattenborg | Evidence that birds sleep in mid-flight[END_REF][START_REF] Rattenborg | Half-awake to the risk of predation[END_REF]. From a behavioral standpoint, sleep is traditionally defined as an inactive state with reduced responsiveness to environmental stimuli (i.e. elevated arousal threshold) that is rapidly reversible in response to sufficient stimulation. In many species, sleep occurs in a species-specific posture and at specific times of the day (Fig. 1. ). The duration and intensity of sleep increases following sleep deprivation, indicating that it is homeostatically regulated [START_REF] Campbell | Animal sleep: a review of sleep duration across phylogeny[END_REF][START_REF] Piéron | Le problème physiologique du sommeil[END_REF]. Initially identified in mammals, two electrophysiological sleep states can be defined during behavioral sleep: paradoxical sleep (PS) or rapid eye movement sleep (REM sleep) and slow wave sleep (SWS) or non-REM sleep (NREM sleep) [START_REF] Aserinsky | Regularly occurring periods of eye motility, and concomitant phenomena, during sleep[END_REF][START_REF] Jouvet | On a stage of rapid cerebral electrical activity in the course of physiological sleep[END_REF]. SWS is distinguished from wakefulness and PS by the presence of high amplitude, low frequency waves in the electroencephalogram (EEG), reduced heart and respiratory rate, reduced brain and body temperature, reduced muscle tone (compared to wakefulness), and the scarcity of eye movements. Environmental awareness is lower compare to resting wakefulness (Fig. 1. ). During PS, the EEG exhibits a desynchronized (low-amplitude, high-frequency) wakelike pattern. In contrast to wakefulness, PS is associated with tonic skeletal muscle atonia [START_REF] Jouvet | On a stage of rapid cerebral electrical activity in the course of physiological sleep[END_REF]. This atonia is phasically interrupted by rapid eye movements [START_REF] Aserinsky | Regularly occurring periods of eye motility, and concomitant phenomena, during sleep[END_REF] and other forms of muscular twitching, particularly in young mammals [START_REF] Corner | Sleep and the beginnings of behavior in the animal kingdom-Studies of ultradian motility cycles in early life[END_REF]. In addition, heart and respiratory rates become irregular during PS [START_REF] Snyder | Changes in respiration, heart rate, and systolic blood pressure in human sleep[END_REF]. Brain temperature increases, but all thermoregulatory mechanisms (i.e. muscle tone, pilo-erection, sweating, and shivering) are abolished [START_REF] Jouvet | On a stage of rapid cerebral electrical activity in the course of physiological sleep[END_REF][START_REF] Parmeggiani | Thermoregulation and sleep[END_REF]. Finally, both SWS and PS are homeostatically regulated. Total sleep deprivation is compensated by an increase in the quantity and intensity of SWS [START_REF] Borbély | Sleep-deprivation: Effects on sleep and EEG in the rat[END_REF], and selective PS deprivation is also followed by a recovery period with more PS [START_REF] Dement | The Effect of Dream Deprivation[END_REF].

Table illustrating the main behavioral, electrophysiological and metabolic parameters that covary with the quiet states (Quiet wake, slow wave sleep and paradoxical sleep) in mammals.

Whereas it is largely accepted that sleep is present in animals ranging from jelly fish to the more complex animals [START_REF] Campbell | Animal sleep: a review of sleep duration across phylogeny[END_REF][START_REF] Nath | The Jellyfish Cassiopea Exhibits a Sleep-like State[END_REF][START_REF] Omond | Inactivity Is Nycthemeral, Endogenously Generated, Homeostatically Regulated, and Melatonin Modulated in a Free-Living Platyhelminth Flatworm[END_REF][START_REF] Raizen | Lethargus is a Caenorhabditis elegans sleep-like state[END_REF][START_REF] Siegel | Do all animals sleep?[END_REF], it is less clear whether all animals display two sleep states. Of the non-mammalian animals investigated, unequivocal evidence of mammalian-like SWS and PS was only found in birds [START_REF] Dewasmes | Polygraphic and behavioral study of sleep in geese: existence of nuchal atonia during paradoxical sleep[END_REF][START_REF] Heller | Circadian and arousal state influences on thermoregulation in the pigeon[END_REF][START_REF] Klein | POLYGRAPHIC STUDY OF SLEEP IN BIRDS[END_REF][START_REF] Rattenborg | Avian sleep homeostasis: Convergent evolution of complex brains, cognition and sleep functions in mammals and birds[END_REF]. Nonetheless, some reports of a PS like state in non-avian reptiles [START_REF] Libourel | Sleep in amphibians and reptiles: a review and a preliminary analysis of evolutionary patterns: Sleep in amphibians and reptiles[END_REF][START_REF] Shein-Idelson | Slow waves, sharp waves, ripples, and REM in sleeping dragons[END_REF], or twitches during behavioral sleep in cuttlefishes [START_REF] Frank | A Preliminary Analysis of Sleep-Like States in the Cuttlefish Sepia officinalis[END_REF] or in bees [START_REF] Klein | Caste-dependent sleep of worker honey bees[END_REF] suggest that two sleep states could also be present in other species.

Classically, sleep is studied in laboratory animal models (rats, mice, cats, dogs, fruitflies, and zebrafish) at various levels of analysis, including genetic, molecular, neuroanatomical, physiological, or cognitive. Several tools are available to assess these aspects of sleep; for example: optogenetic, immunohistochemical, behavioral tests, neuronal activity recordings, LFP, EEG, EMG, ECG, EOG, and temperature measurements. However, of the species in which sleep has been studied (less than 200 of the approximately 66 000 vertebrates), most of them were studied in to the lab via a tethered device that recorded EEG and EMG. A weakness of this approach is that using a tether could induce stress by reducing the animal's freedom of movement and precluding the use of sleeping shelters [START_REF] Tang | Effect of varying recording cable weight and flexibility on activity and sleep in mice[END_REF]. Therefore, wireless alternatives are obviously required to reduce stress and foster more natural sleep patterns. Since 2000, one logger [START_REF] Vyssotski | Miniature Neurologgers for Flying Pigeons: Multichannel EEG and Action and Field Potentials in Combination With GPS Recording[END_REF][START_REF] Vyssotski | EEG Responses to Visual Landmarks in Flying Pigeons[END_REF] http://www.vyssotski.ch/neurologger.html) and several telemetric systems with a limited transmission range [START_REF] Lapray | A novel miniature telemetric system for recording EEG activity in freely moving rats[END_REF][START_REF] Tang | Telemetric Recording of Sleep and Home Cage Activity in Mice[END_REF][START_REF] Weiergräber | Electrocorticographic and deep intracerebral EEG recording in mice using a telemetry system[END_REF][START_REF] Zayachkivsky | Recording EEG in immature rats with a novel miniature telemetry system[END_REF] have been developed to record sleep-related EEG and EMG activity in rodents. The telemetry devices could record 1 or 2 channels at a low sampling rate (<1 kHz) for periods lasting from days to months, in the case of devices with capacitive wireless transmission [START_REF] Tang | Telemetric Recording of Sleep and Home Cage Activity in Mice[END_REF]. Other systems can record more channels (>32) at a higher sampling rate (>10kHz) needed to record neuronal activity [START_REF] Harrison | Wireless Neural/EMG Telemetry Systems for Small Freely Moving Animals[END_REF][START_REF] Hawley | Telemetry system for reliable recording of action potentials from freely moving rats[END_REF][START_REF] Mohseni | Wireless Multichannel Biopotential Recording Using an Integrated FM Telemetry Circuit[END_REF][START_REF] Sodagar | An Implantable 64-Channel Wireless Microsystem for Single-Unit Neural Recording[END_REF][START_REF] Yin | Wireless Neurosensor for Full-Spectrum Electrophysiology Recordings during Free Behavior[END_REF]. Nevertheless, with a battery of an acceptable size for a laboratory rodent to carry, these systems can only record for a few hours, which is insufficient for most sleep studies wherein disturbing the animals should be minimized. In general, it is essential that a sleep recording device records for at least 48 hours. Moreover, for comparative studies of species that do not exhibit sleep states readily comparable to mammalian SWS and/or PS based on EEG and EMG activity alone, it is important to record as many parameters as possible to facilitate comparison with mammals. Consequently, in the case of comparative sleep studies, behavioral, electrophysiological and metabolic parameters should be recorded in order to provide more informative features regarding how an animal sleeps. Unfortunately, none of the existing devices has enough channels to record multiple EEG, EMG, ECG and EOG channels, LFPs, and brain and body temperature for at least 48 hours, Moreover, none incorporates a means to evaluate the behavioral criteria of sleep, such as arousal threshold, reversibility, and homeostasis. Another important constraint is the weight and the size of the device. Obviously, all devices could record for a month with large batteries. However, most species are small and cannot carry heavy systems. Therefore, to increase autonomy, the power consumption of the system should be minimized.

In addition to these constraints, if the aim is to record the animal in the wild, other parameters are needed to describe sleep behavior. In the lab we often record video in conjunction with the electrophysiology, but in the wild, this cannot be done easily. An alternative is to record the animal's head movement with accelerometry along the three axes [START_REF] Rattenborg | Sleep research goes wild: new methods and approaches to investigate the ecology, evolution and functions of sleep[END_REF]. Moreover, as the quantity of sleep and the presence of topor/hibernation are temperature dependent, it is also important to record naturally occurring changes in ambient temperature in the wild. However, the major difference between recording in the lab and the wild is the manner in which the data is stored. In the lab, movement is usually restricted to a small space from which data can be transferred and stored on a computer via wireless transmission. However, in the field or in large enclosures [START_REF] Lesku | Ostriches Sleep like Platypuses[END_REF] that exceed the transmission range of small telemetry devices, the data needs to be logged on the device [START_REF] Rutz | New frontiers in biologging science[END_REF]. Currently, only one device available in various versions (http://www.vyssotski.ch/neurologger.html), is small enough, to record EEG and EMG combined with 3D accelerometry for sleep studies [START_REF] Vyssotski | Miniature Neurologgers for Flying Pigeons: Multichannel EEG and Action and Field Potentials in Combination With GPS Recording[END_REF]. This device has been used successfully to record sleep-related electrophysiology and behavior in the field in sloths [START_REF] Rattenborg | Sleeping outside the box: electroencephalographic measures of sleep in sloths inhabiting a rainforest[END_REF][START_REF] Voirin | Ecology and Neurophysiology of Sleep in Two Wild Sloth Species[END_REF], sandpipers [START_REF] Lesku | Adaptive Sleep Loss in Polygynous Pectoral Sandpipers[END_REF], barn owl chicks [START_REF] Scriba | Evaluation of two minimally invasive techniques for electroencephalogram recording in wild or freely behaving animals[END_REF], and even frigatebirds in flight [START_REF] Rattenborg | Evidence that birds sleep in mid-flight[END_REF]. Although this device opened the door for the first field-based sleep studies and remains a powerful tool for many field-based sleep studies, the low number of channels (four) and the absence of a device for assessing arousal thresholds and disrupting sleep limits the scope of the questions that can be addressed with this device.

In summary, sleep is a universal and complex state, characterized by behavioral, electrophysiological and metabolic changes from wakefulness. Currently, multiple tools can assess some features of its phenotype in lab or more rarely in the wild, but none of them can measure the behavioral, electrophysiological, and metabolic features of sleep at the same time, in small species, for long periods. A device with these capabilities is needed for comparative and ecological experiments aimed at understanding the evolution and functions of sleep. Therefore, we developed ONEIROS (ONE Instrument for Recording Our Sleep), a telemetric/datalogger system designed for sleep studies. This system is small enough to be worn by rats. The device can record 26 referential channels of electrophysiology (EEG, EMG, ECG, EOG or LFP), three temperature channels, and 3-axial accelerometry. Moreover, to evaluate the arousal threshold of to enforce sleep deprivation, the system includes a lightweight, vibrating motor. ONEIROS weighs less than 10g, when configured with a battery capable of recording for more than 48 hours. To validate the system, we simultaneously recorded for the first time the EEG, EMG, EOG, 6 LFPs in the hippocampus, the brain and body temperature, and 3D head acceleration of a rat. We also performed selective PS deprivation on a rat for 6 hours by activating the vibrating motor fixed on the device when PS was automatically detected via a custom online sleep scoring algorithm [START_REF] Libourel | Unsupervised online classifier in sleep scoring for sleep deprivation studies[END_REF]. We compared the effect of the deprivation with published data obtained with gentle handling and automated deprivation methods. Finally, to demonstrate the feasibility of recording in the wild, we recorded a pigeon in a large aviary with other birds.

II. METHODS

Surgery and experimental recording conditions

Ethical considerations

All experiments were conducted with the 3R principles in animal experimentation and in accordance to the European Community Council Directive for the use of research animals (86/609/EEC and 2016/63/EU).

Rat baseline

Under ketamine-xylazine anaesthesia (100mg.kg -1 -10mg.kg -1 respectively, I.P.), one Sprague Dawley male adult rat (230g, Charles River Laboratories, France) was placed on a stereotaxic frame (David Kopf Instruments, USA) and implanted for with sensors for recording the EEG, EMG, and EOG, as well as body and cerebral temperatures. Following incision of the scalp and removal of the skin, holes were drilled in the skull. EEG monitoring: two stainless steel screws (Bilaney, Plastics One, Germany) were fixed in the parietal (from bregma: anterior-posterior (AP), -4mm; medial-lateral (ML), +3mm) and frontal (AP, +3mm; ML, + 1mm) parts of the skull and two above the cerebellum (AP, -12mm; ML, +3mm) served as references. In addition, a 4-electrode bundle was placed in the hippocampus for LFP recordings. It was composed by 4-tungsten wires (35µm in diameter, Scientific Wire Company, England) with different lengths (500µm of difference for each). The bundle was inserted with the following coordinates: AP, -3.8mm; ML, +1.8mm, and dorsal-ventral, -4mm, to record from the lower part of the dentate gyrus (longest wire) to the CA1 region (shortest wire) of the hippocampus.

The screwscrews and bundle were fixed on the skull and electrically insulated from one another using acrylic Superbond (Sun Medical Co, Japan). EMG monitoring: two gold-coated electrodes were inserted into the neck muscles. EOG monitoring: two wires with gold-coated thin ball ends (1mm in diameter) were bilaterally placed under the eyelid, close to each eye. The wires were fixed on the skull with Superbond. Cerebral temperature monitoring: one additional hole was drilled in the occipital part of the skull and a thermistor (GA100K6MCD1, Measurement Specialties) was inserted close to the brain. The hole was filled with bone wax. Body temperature monitoring: one thermistor was inserted deeply between the neck muscles and secured with a suture. All wires were then connected to a head connector (Electronic Interface Board-36-PTB Neuralynx), which was secured to the skull using Superbond acrylic. Next, dental Paladur cement (Heraeus Kuzler) was applied around the head connector to protect all of the wires and the connector. At the end of the implantation procedure, the rat received a non-steroidal anti-inflammatory injection (carprofene, 5mg.kg-1, S.C.) and was allowed to recover for 7 days, during which it was weighed and monitored daily. Then, the rat was housed in a Plexiglas barrel (30cm in diameter, Blox Usinage Plastique, France) with bedding, food and water ad libitum placed in a recording chamber with a 12h/12h light-dark cycle, ventilation, and a 23°C ambient temperature. ONEIROS was plugged into the animal's implant and baseline recordings started after 2 days' habituation to the device and the new environment. Signals were collected using DaqReverse, a custom Matlab (Mathworks, matlab r2016b) program, and were sampled at 256 Hz except for the temperature and accelerometer which were sampled at 64 Hz.

Vigilance states were scored using SlipAnalysis a custom Matlab program with a 5-s sliding time frame window according to the following criteria: Active wake (AW) was characterized by desynchronized and irregular lowvoltage and high-frequency (5-9 Hz) EEG activity, sustained EMG neck muscle tone, and movement detected by the accelerometer. EEG activity was similar during AW and Quiet wake (QW), but QW was differentiated from AW by the absence of movement. Slow-wave sleep (SWS) was characterized by high-voltage slow-waves (1.5-4 Hz) combined with low muscle tone similar to QW. Paradoxical sleep (PS) was characterized by a very regular theta rhythm (5-9 Hz) associated with muscle atonia (absence of muscle tone and accelerometer activity).

Rat Paradoxical Sleep deprivation

One Sprague Dawley adult rat (male, 270g) was used. The surgical procedure was the same as previously described. Briefly, four screws for EEGs were fixed on the skull bilaterally over parietal and frontal cortices and two screws over the cerebellum for references. Two EMG and two EOG electrodes, and brain and body thermistors were also implanted. At the end of the implantation procedure, the rat received a non-steroidal antiinflammatory injection (carprofene, 5mg.kg-1, S.C.) and was allowed to recover for 7 days, during which it was weighed and monitored daily. The rat was first placed in the recording chamber for baseline recording. Data were sampled at 128 Hz except for temperature and accelerometry which were sampled at 64 Hz. The baseline signals were scored and template parameters for each state were extracted from the EEG and EMG for online sleep scoring [START_REF] Libourel | Unsupervised online classifier in sleep scoring for sleep deprivation studies[END_REF]. To enforce PS deprivation (PSD), we used an online algorithm to detect PS [START_REF] Libourel | Unsupervised online classifier in sleep scoring for sleep deprivation studies[END_REF] and a vibrating motor embedded on the ONEIROS device (Fig. 2. A) to awaken the rat when PS was detected. The vibration intensity was set to 100% and the stimulation duration was 700 ms. After a 6-h period of PSD, the rat was recorded for an additional 6-h recovery (PS recovery -PSR). The time spent in each vigilance state was quantified during baseline, PSD, and PSR.

Pigeon baseline

One adult pigeon (one female, Columbia livia, 250 g) was anesthetized using isoflurane, then placed in stereotactic device (David Kopf Instruments, USA) and instrumented for EEG, EMG, EOG recordings. For EEG monitoring, four gold-plated, round-tipped (0.5 mm diameter) electrodes (Bürklin, Germany) were placed over the anterior and posterior hyperpallium (Wulst), a primary visual area. The electrodes were symmetrically placed, 4 mm apart along the AP axis and 2.5 mm and 3 mm from the midline for the anterior and posterior electrodes, respectively. Two electrodes were placed above the left and right sides of the cerebellum served as references for the ipsilateral EEG electrodes. For EOG monitoring, two electrodes were placed in the porous bone cavity behind the top of the eye; the electrodes did not enter the orbit. After positioning, the EEG and EOG electrodes were secured with dental acrylic. For EMG monitoring, two wire electrodes were inserted into the nuchal neck muscles. To ensure a good adhesion between the dental acrylic and the bone, small holes were drilled through the top layer of the cranium, which allowed the acrylic to infiltrate the bone. The electrodes, cables and the connector were embedded in dental acrylic. At the end of the implantation procedure, the bird received an intramuscular injection of meloxicam (Metacam 2mg/kg) for post-operatory analgesia. The pigeon was allowed to recover for 24 hours before attaching the data transmitter to the connector on its head. For the recordings, the device was equipped with a 1 Gb SD card and was attached to the connector on the bird's head. The pigeon was then placed in an all-female group aviary (2 m x 2 m x 2 m) together with another three uninstrumented birds. The aviary was equipped with an infrared camera for video monitoring (Axis M20 Network Camera Series). The EEG, EMG, EOG and 3-axis accelerometry were recorded in logger mode at 256 Hz for 24 hours. At the end of the recordings, the bird was recaptured, the device was removed and the data collected from the SD card. All animals had ad libitum access to food and water. The aviary had a 12h/12h light-dark cycle and an ambient temperature of 20°C.

A new device to quantify sleep

Embedded system for data acquisition

ONEIROS was designed to provide a flexible set of tools, fitting in a tiny device, for the analysis of sleep in small animals. It includes an integrated, low-power electrophysiology frontend to measure up to 26 biosignals, 3 temperature signals, and 3 accelerometer axes. An additional vibrating motor can be connected to the system and controlled using either real-time or predefined sequences of variable durations and intensities of vibration to assess arousal thresholds or prevent sleep. The system can be used either as a data logger, by using an embedded media storage, or as a telemetry device for real-time monitoring and analysis of the signals. The overall hardware architecture and software embedded on the system were developed to ensure that it would match the requirements (number of channels needed and bandwidth) of various possible experimental conditions and animal species. The size of the entire electronic system is 9 mm x 16 mm x 25 mm and it weighs 4 g.

Together with a small 3 Volts, 150 mAh Li/MnO2 primary battery of 1.4 g (CP251525, GMB Company Ltd.) it can be encapsulated in a 28 mm x 18 mm x 15 mm plastic enclosure (Fig. 2. B). The addition of the vibration motor requires an additional width of 2 mm on one side of the enclosure and a weight of 1 g.

A. Picture of the device showing the stack of boards with circular vibrating motor connected. B. Picture of a rat with the enclosed wireless device on its head.

Hardware boards

To enable rapid changes and optimizations of the main system functions, we designed small printed circuit boards which can be stacked together to form a functional measurement system (Fig. 2. A). The boards are interchangeable allowing adaptation of the system to specific experiment conditions. This modularity also allows for future improvements of the main functions. In the current version, four boards form a fully working system: a power stage, which provides independent power from the battery for the analog and digital boards; a communication stage which consists of either an embedded memory storage board, or a wireless data transfer board; an analog stage which contains the frontend for the acquisition of biosignals; and, finally, a digital stage which controls and synchronizes data acquisition from the analog stage and transfer to the communication stage.

The connection between each stage is made by stackable connectors on a single side or on both side of the board depending on the position of the board in the system (for example, the power board contains connectors only on the bottom side as it is the upper stage of the system). The global interconnection between all boards is shown on

Power board

The power board is placed at the top of the system stack, together with the battery. The electronic circuit is mainly composed of a dual channel boost-converter (LTC3535, Linear Technology Corporation) to provide constant voltages for analog and digital power supplies from the voltage of the battery, which can vary from 3.2 V down to 2 V at the end of the battery life. Due to the voltages required by the communication and analog frontend stages, the boost-converter is configured to output 3.3 V for the digital power supply with up to 100 mA output current (required in case of the use of a micro-SD card in the communication stage). On the other hand, the second channel of the boost-converter is configured to output 3.6 V for the analog power supply, which is subsequently fed into a 3.3 V low-noise, low-dropout regulator (LDO). The dedicated LDO (LP5907, Texas Instruments Inc.) is used to minimize the output ripple of the boost-converter on the analog power supply, as the amplifier stage of the analog frontend board can be very sensitive to power supply variations and noise. The power board also contains a light indicator (low power LED) connected to the digital connector stack and can be controlled by the microcontroller and used to indicate the state of the device. The power switch is a Hall Effect, bidirectional latch (AN48846B-NL, Panasonic Corporation) which can be opened and closed using a proximity magnet. This enables control of the power board even when the system is enclosed in a waterproof housing.

Communication board

Two communication boards have been developed and can be exchanged depending on the environmental conditions of the measurement as well as the requirements of the experiment. The first board is composed of a micro-SD card interface, and enables the embedded recording of acquired data on a memory card. With this board, the system is used as an autonomous data-logger system mounted on the animal; the data is retrieved from the memory card after the experiment. The second board developed is composed of a 2.4 GHz transceiver (nRF24L01p, Nordic Semiconductors) and an associated radio-frequency (RF) circuit and ceramic chip antenna.

When assembled with the RF communication board, the system is used as a wireless telemetry system and the data are collected in real-time by using a base-station receiver connected to a computer via a Universal Serial Bus (USB) cable. As described, the modularity of the system enables it to record in laboratory conditions, with real-time acquisition of the data, as well as in a natural or semi-natural environment where the animal can freely move with the device in the data-logger configuration.

Analog frontend board

The analog frontend board is intended to be placed at the lower stage of the board stack, as the connector (Omnetics, Dual Row Nano Strip series, 32 contacts) is located on the bottom side of the board for linking the electronic interface board (EIB) to the electrodes and temperature sensors on the animal. The board is based on the RHD2132 (Intan Technologies, LLC.) digital electrophysiology interface chip. This low-power, integrated circuit contains a 16-bit analog/digital converter (ADC), a 32 channels low-noise amplifier with programmable bandwidths and 3 additional auxiliary inputs. The 3 auxiliary channels are used for temperature measurements with negative-temperature coefficient thermistors of 100 kOhm at 25 °C. Due to hardware (EIB connector) and software limitations, only 26 amplified channels are used for electrophysiology measurements which make a total of 32 channels when combined with temperature (3 channels) and three accelerometer axes. Data acquisition is triggered independently for each channel, and the ADC result is retrieved using the serial communication bus connected to the micro-controller board. This enables each channel to be independently sampled at different rates as described in the software section. This flexibility allows the user to adjust the power consumption of the device through changing the sampling rate required for each biosignal, temperature, and accelerometer axis.

Microcontroller board

The microcontroller board contains the microcontroller unit (MCU) and the digital accelerometer integrated chip, as well as a driver for the vibrating motor. The accelerometer is an ADXL362 (Analog devices, Inc.), a 3axis MEMS accelerometer with a resolution of 12 bit and an average active consumption of 2 µA. The microcontroller used in the system is a MSP430FR5969 (Texas Instrument, Inc.). This microcontroller was selected due to its very low-power consumption of 100 µA/MHz and its flexibility in terms of power management (1 active and 3 low-power modes of operation). The software embedded on the microcontroller is described in the section 2.7. The microcontroller is connected to three communication serial buses using the Serial Protocol Interface (SPI) to control and transfer data from the accelerometer, the analog frontend board, and the communication board. Additionally, the microcontroller is directly connected to a universal haptic drive (DRV2603, Texas Instruments Corporation) which controls an 80 mA vibration motor that can be connected directly to the microcontroller board.

Functional diagram of the complete instrumentation

Software embedded on the microcontroller

The software embedded on the microcontroller was developed using the Code Composer Studio integrated development environment (Texas Instrument, Inc.). The software was written in C language, compiled using the TI compiler and transferred to the microcontroller with an EZ-FET programmer for the MSP430 microcontroller family. The role of this software is to provide flexibility and adaptability of the instrumentation to the specific needs of each experiment (i.e. the number of channels used among the 32 channels available, and a dedicated sampling frequency for each individual channel). As a result, this method of acquisition has the advantage of minimizing the power consumption of the entire system when less channels or lower sampling rates are required, thus extending the autonomy of the device for longer experiments.

At startup, the first task of the software is to detect whether the telemetry or the logger board is used for recording data and to configure the peripherals accordingly. Then the program enters an infinite loop where three different states are executed sequentially: a) retrieving the sampling rate of each channel, b) generating an acquisition sequence based on the table of sampling frequencies, and then c) indefinitely acquiring and transferring samples (following the generated sequence) until the system is stopped by power down or configuration change. If using a telemetry configuration, where data are transmitted to a remote computer, the software is able to receive real-time orders to control the vibration motor. In that case, an algorithm executed on the remote computer can automatically use the data received from the system to, for example, classify awake or sleep states of the animal and then send back vibration commands to induce sleep deprivation or to assess arousal threshold. Fig. 5. illustrates the overall execution of the program with annotations regarding telemetric or logger specific usages.

Flowchart of the embedded software on the micro-controller unit

Signals description and acquisition

The device enables the acquisition of 32 different channels of three different types: a) 26 channels for biosignals, including a reference channel, b) 3 channels for temperature based on negative temperature coefficient (NTC) thermistors, c) 3 channels for accelerometry based on a 3-axis digital accelerometer. Due to limited wireless bandwidth, and also to limited power consumption when using the logger configuration, the overall maximum sampling frequency is set to 8192 samples per second (sps). This maximum sampling rate can be split over the different channels via software (Fig. 6. ) in any combination as long as the sum of all frequencies is equal to or lower than 8192 Hz (for example, 1 channel sampled at 8192 sps or 8 channels sampled at 1024 sps). Based on the sampling rate defined for each channel individually, the embedded software automatically generates a sequence of acquisition at a fixed clock rate with respect to every channel frequencies.

For timing precision, a 32.768 kHz crystal oscillator is used with a drift of only 20 ppm or less. Then every possible sampling frequency that is an integer divisor of this clock (8192, 4096, 2048, 1024, etc.) can be set.

When sent using wireless communication, data are collected using a RF remote receiver connected to a remote computer using a USB connection. The remote receiver is composed of the same transceiver chip (nRF24L01p) as the transmitter, and is associated to a PSoC 5LP (Cypress Semiconductors) microcontroller unit which transmits sampling rate configurations to the system and collects data frames which are subsequently sent to the computer through USB port. On the host computer, a dedicated driver as well as graphic user interface have been developed using Matlab (The Mathworks) to facilitate configuration, real-time visualization and control, as well as storage of the data (Fig. 6. ). digital precision of 0.195 µV and total voltage range of +/-6.39 mV. Integrated and configurable analog bandpass filters prevent aliasing effects and enable DC removal.

Temperatures

The analog frontend contains three auxiliary (not amplified) analog channels used to acquire temperature measured with NTC, 100kOhm thermistors. The thermistors (GA100K6MCD1, Measurement Specialties) are polarized with voltage from a voltage divider by using a 100kOhm resistor (Fig. 7. ). Auxiliary inputs are sampled through the same analog to digital converter as the biosignals, but with a voltage range of 0 -2.4V.

When using the voltage divider to polarize the thermistor, the linearity error is lower than 1% in the range 15 -40°C, and variations lower than 0.002 °C can be measured.

Temperature measurement circuit diagram

Accelerometry

A 3-axis, 12-bit digital accelerometer is used to assess movement of the animal's head. By using a range of +/-1g, the accuracy of the measurement is 0.001g. Internal bandwidth can be configured depending on the sampling frequency to avoid aliasing. Data are directly retrieved by the microcontroller through a serial peripheral interface (SPI).

Electrical characteristics

Although analog signal processing, analog to digital conversion, and digital interfaces with the microcontroller contribute to the overall power consumption of the system, for the most part, the autonomy of the system will depend on the communication board. Whether it uses RF communication or SD-Card storage, this stage has much higher power consumption when handling data. Hence, its consumption depends on the amount of data collected by the system; i.e. the total sampling rate of the configuration. In order to predict the autonomy of the system, the overall consumption has been accurately measured for different sampling frequencies (Fig. 8. ).

Power consumption of the system with a 3V supply voltage and expected autonomy with a 150mAh primary battery.

III. RESULTS

Multiple parameters recorded in baseline condition with ONEIROS (telemetric version) in a rat

To validate the design of our system (size, weight, signal quality, and autonomy), we first monitored a rat in standard lab conditions. Here, for the first time we were able to record at the same time most of the electrophysiological parameters that covary with the different states of vigilance. Fig. 9. A. illustrates 90s of raw signal recorded with ONEIROS and Fig. 9. B. shows 10s of each state. The characteristics and quantity of each state were consistent with previously published data (Fig. 10. ). During active wake (AW: dark blue), EEG and hippocampal activity is desynchronized, with the later also showing a sustained theta frequency (around 6Hz, see time frequency plot) characteristic of periods of locomotion (see also Fig. 9. and Fig. 10. D.) [START_REF] Sławińska | The frequency of rat's hippocampal theta rhythm is related to the speed of locomotion[END_REF]. The EMG, EOG, and the accelerometer show bursts of intense activity. The brain and nuchal temperature are at high levels. During QW (light blue), the animal stops moving, as reflected in the EMG and accelerometry recordings. Eye movements become less frequent and the EEG tends to increase in amplitude and decrease in frequency when compared to AW (Fig. 10. B). During SWS (red), the EEG is dominated by large slow waves (Fig. 9. and Fig. 10. D). During SWS, the EMG remains at a low level, the accelerometry shows little variation, indicating the absence of movement, and eye movements are nearly absent. Both temperatures tend to decrease during SWS. When the animal falls into PS (green), the parietal EEG and hippocampal recording show typical activity in the theta band (Fig. 9. and Fig. 10. D), eye movements occur, the EMG becomes atonic, the brain temperature increases to wake levels, but body (nuchal) temperature continues to decline (Fig. 9., Fig. 10. C). A small nuchal "twitch" is also visible on the accelerometer signal (Fig. 9. B).

Although high voltage waves were observed when the animal knocked the device against the cage wall, the signals were usually free of artefacts, even during grooming and locomotion.

Selective paradoxical sleep deprivation in a rat using ONEIROS

The vibrating motor embedded in the ONEIROS device (Fig. 2. A.) was used to evaluate its efficacy in suppressing PS for several hours and inducing a homeostatic increase in PS during post-deprivation recovery.

We performed selective PS deprivation for 6 hours by using a probabilistic online sleep scoring algorithm [START_REF] Libourel | Unsupervised online classifier in sleep scoring for sleep deprivation studies[END_REF]. Fig. 11. illustrates the raw signals during the PS deprivation (PSD) experiment (see also supplementary video 1). After a few seconds of PS (see atonia and EEG desynchronization), the algorithm sends a stimulation (white bar on the bottom bar) that immediately awakens the animal (supplementary video 1). The latency to detect PS was around 3-4 seconds. Fig. 11. B. shows the percentage of PS per hour during and after the deprivation (in green) compared to baseline conditions (in blue). The deprivation reduced the quantity of PS for 6 hours by 60% (58% in baseline to 23% during PSD). Moreover, PSD also induced a rebound of PS (PSR) quantity during the recovery period with an increase of 85% over baseline levels (14% in baseline vs 26% during PSR,Fig. 11. D.). Compared to 4 hours of PSD via gentle handling [START_REF] Ravassard | REM Sleep-Dependent Bidirectional Regulation of Hippocampal-Based Emotional Memory and LTP[END_REF], Fig. 11. E) and unpublished data from our lab using a mechanical shaking device (Viewpoint S.A., [START_REF] Libourel | Unsupervised online classifier in sleep scoring for sleep deprivation studies[END_REF], the quantity of remaining PS during PSD with ONEIROS is higher (5% with ONEIROS compared to 2.6% and 2.7% with the other methods). This is due to the presence of PS episodes during which the stimulations didn't immediately awaken the animal. However, PS quantities during the recovery were consistent with those obtained with the other methods. hours of PS deprivation enforced via gentle handling [START_REF] Ravassard | REM Sleep-Dependent Bidirectional Regulation of Hippocampal-Based Emotional Memory and LTP[END_REF], 6 hours of automated PS deprivation induced by a custom shaking device [START_REF] Libourel | Unsupervised online classifier in sleep scoring for sleep deprivation studies[END_REF], and ONEIROS PS deprivation.

Multiple parameters recorded in baseline condition with ONEIROS (logger version) in a pigeon

The electrophysiological and behavioral aspects of sleep recorded with ONEIROS in the pigeon were similar to those recorded in birds using other methods. Fig. 12. illustrates raw recordings with alternating periods of QW and AW, followed by SWS. The periods of AW were characterized by increased muscle activity and increased motion (visible on the accelerometry channels), and desynchronized EEG activity. The two peaks at 12 Hz and 24 Hz present on the power spectrum of AW (Fig. 12. A) resulted from head scratching. The periods of QW were characterized by low muscle activity (Fig. 13. B), comparatively small and infrequent changes in the accelerometry signal, generated by the birds' head movements, and desynchronized EEG activity. During SWS the EEG showed increased low frequency activity, when compared to all other states, increased Delta/Gamma ratio (Fig. 13. C) and the near absence of motion. Muscle tone usually remained at a level comparable to QW.

PS was characterized by EEG activation and behavioral signs of reduced muscle tone (e.g. head dropping as shown in the accelerometry recordings). As in other studies on pigeons and other avian species, the nuchal EMG rarely showed a reduction in activity, despite the behavioral signs of reduced tone. The number of eye movements increased during PS when compared to SWS. As previously described in pigeons and other birds (e.g. [START_REF] Dewasmes | Polygraphic and behavioral study of sleep in geese: existence of nuchal atonia during paradoxical sleep[END_REF][START_REF] Tobler | Sleep and EEG spectra in the pigeon Columba livia under baseline conditions and after sleep deprivation[END_REF], eye movements included saccades, as well as faster oscillations (at [START_REF] Vii | [END_REF][26][27][28][29][30]. Unlike saccades which rarely occurred during SWS, the fast oscillations occurred during all sleep and waking states. They are thought to disperse oxygen and nutrients in the vitreous humor of the avian eye by moving a membrane (pecten) that protrudes inside the vitreous humor [START_REF] Pettigrew | Saccadic oscillations facilitate ocular perfusion from the avian pecten[END_REF].

Overall, the duration and timing of sleep bouts, as well as the amount of each state was typical for pigeons (Fig. 13. E). During the 12-h night, the bird spent 82.9% and 9.5% of the time in SWS and PS, respectively (Fig.

IV. DISCUSSION

1. Recording electrophysiology, behavior and temperature using a miniature instrument One of the main reasons for developing ONEIROS was to combine in one small, light wireless device all of the electronics required to record multiple parameters related to sleep for long periods of time in small animals.

Technically, the main constraints are size, weight, and autonomy. By coupling a frontend designed to record electrophysiology, an integrated digital accelerometer, a circuit to record temperature from multiple thermistors, and a low power microcontroller, we were able to build a system for studying sleep in small animals. Our recordings in baseline conditions in a rat, demonstrated that the size, weight, and autonomy of the system meet the requirements for recording sleep in the laboratory setting. Indeed, for the first time we were able to record without artefact in a freely moving animal most of the physiological and metabolic parameters that covary with sleep states (EEG, EMG, EOG, ECG, LFPs, and brain and body temperature). Moreover, we were also able to record the posture and the acceleration of the head via a 3 axis accelerometer, as well as ambient temperature.

Our results and analysis demonstrate that an overall view of the classical features of sleep can be obtained with a single device. For example, the system can simultaneously record 10 electrophysiological channels at 256 Hz, three temperatures, and three acceleration channels at 64 Hz, for 35 hours with a 150 mAh; or 64 hours with 4 EEG, 1 temperature and 1 acceleration at 128Hz; an infinite number of other recording configurations are also possible. To our knowledge, in contrast to other commercial and/or published devices, ONEIROS is the only one that provides such flexibility in the configuration of the channels number and sampling rate, allowing users to completely customize data acquisition according to their needs.

In terms of limitation, while the system is small enough to be worn by rats, it is too large to be used on smaller animals such as mice. We estimate that the device is only suitable for animals over 100g. Future efforts should be directed toward reducing the size and weight of the device further, maybe with some compromises in term of capability and flexibility. Regarding the fields of application, although the system was designed for recording sleep, it could be used for other neuroscience applications. Behavioral tasks such as mazes, novel object recognition, and fear conditioning that require freely moving animals, might also benefit from the use of ONEIROS. Moreover, the device might be useful in studies using animal models of epilepsy wherein long-term recordings are needed to capture seizure related brain activity and behaviors.

Sleep deprivation with ONEIROS

For the first time a vibration motor integrated in a wireless recording device has been used to perform automated, real-time PS deprivation. Our results indicated that the stimulation was intense enough to awaken the animal and thereby induce sleep deprivation. The system effectively reduced PS across a 6-hour period, and induced a homeostatic increase in PS following deprivation similar to that observed using other methods, such as gentle handling and shaking the floor of the animal's cage. However, the remaining quantity of PS during PSD was a bit higher compared to the other methods, likely because the stimulation was less intense compared to gentle stimulation or cage shaking. This also suggests that it might be difficult to awaken the animal with the device and settings used during longer term sleep deprivations. Possible solutions to this problem would be either to increase the duration of the stimulations (700ms in our experiments) or randomized the pattern of the stimulations. Another possibility would be to encapsulate the vibrating motor inside the dental cement, directly over the head of the animal.

Evaluation of the arousal thresholds with ONEIROS

In addition to the main parameters that characterized sleep (electrophysiology, posture, temperature), including its homeostatic regulation, arousal threshold is also an important feature of sleep. With ONEIROS, the intensity, occurrence, and duration of the vibrating motor stimulation can also be specified, allowing for the systematic assessment of arousal thresholds.

Recording sleep in semi natural environment with ONEIROS

By changing the wireless transmission stack to the logger stack, the device can be quickly transformed from a lab-based device to a logger suitable for recording sleep in the field. In this regard, ONEIROS does not need any additional systems to store the data (receiver, computer), as the signals are stored directly on an integrated SD card. In the logger configuration, sleep can be recorded in wild using methods previously employed with data loggers having fewer capabilities [START_REF] Rattenborg | Evidence that birds sleep in mid-flight[END_REF]. To demonstrate the feasibility of recording sleep in the field, we implanted a pigeon and recorded its vigilance states in an aviary where other birds were also housed. The instrumented bird displayed normal behavior including short flights in the 2 x 2 x 2 m aviary. Thus the system could be used in a completely natural environment, as previously done with a logger with fewer recording capabilities [START_REF] Lesku | Adaptive Sleep Loss in Polygynous Pectoral Sandpipers[END_REF][START_REF] Rattenborg | Evidence that birds sleep in mid-flight[END_REF][START_REF] Vyssotski | EEG Responses to Visual Landmarks in Flying Pigeons[END_REF]. In comparison to this system, ONEIROS includes the capacity to record temperature, a useful parameter for evaluating the relationship between ambient temperature, body temperature, and sleep (SWS and PS) and hibernation or torpor under natural conditions. In addition, it can record more channels, which is necessary when recording sleep in a species for the first time. ONEIROS provides more sleep parameters combined in a single miniature device than other devices used for recording into the wild. Future improvements of ONEIROS will include a recording scheduler, in order to define specific recording periods. For example, this could be used to exclude the post-operative recovery period, and thereby extend the recording duration capability of the device. Moreover, we plan to develop waterproofing and other protections necessary for recording in the wild.

V. CONCLUSION

ONEIROS was developed to record multiple aspects of sleep (behavior, electrophysiology, metabolism) from animals in the lab and the wild. The goal was to provide researchers with a tool that overcomes the limitations of existing wireless devices. The system provides high flexibility in terms of number of channels and sampling rate with low power consumption, allowing long-term recordings in small animals (from 100g). By wirelessly recording a rat under baseline conditions, performing a paradoxical sleep deprivation experiment in a rat, and logging data under semi-natural conditions in a pigeon, we demonstrated that ONEIROS is a useful tool for recording sleep under diverse conditions. For the first time EEG, EMG, EOG, ECG, LFPs, 3D acceleration, brain, body and ambient temperature recording, as well as homeostatic and arousal threshold experiments, can be conducted with the same system in the lab and in the wild on small animals. By facilitating comprehensive comparative and ecological studies of sleep, this device may lead to new perspectives regarding the evolution and functions of sleep.

Fig. 3 .

 3 Fig. 3. Any board can be replaced by any other one of the same type, as long as the connectors' placement and pinout is retained. To minimize noise on the analog signals, the analog power supply and the serial bus to the analog frontend are placed on the first stack of connectors. The digital power supply, control lines and serial bus to the digital boards are placed on the second stack of connectors at the opposite of the circuit board. The overall functional diagram of the current version of the system is shown on Fig. 3.

  Raw signals obtained with ONEIROS in telemetric mode from a rat during different vigilant states. A. 90 second recording of all signals. From the top to the bottom; hypnogram illustrating the wake/sleep scoring (active wake in dark blue, quiet wake in light blue, slow wave sleep in red, and paradoxical sleep in green); parietal EEG; time frequency representation of the hippocampal local field potential (color coded from -131 dB in blue to -73 dB in red); EMG with a high pass filter (cutoff frequency 10Hz, order 2); EOG; brain temperature; nuchal temperature; ambient temperature; 3 axial accelerometry. B. Representative 10 second examples of the parietal EEG, hippocampal LFP, EMG, EOG and acceleration along x axis, occurring during the four states. Results over 24 hours obtained from telemetric recordings in a rat. A. Quantification of the vigilant states over 24 hours. B. Number of eye movements per minute per state. C. Distribution of the muscle tone per epoch for each state. D. Power spectrum in each state computed for the parietal EEG and hippocampal LFP.

Paradoxical

  Sleep deprivation efficiency. A. Hypnogram showing vigilance states (wake in blue, Slow wave sleep (SWS) in red, and Paradoxical Sleep (PS) in green), raw EMG signal, EEG time frequency and the associated raw EEG signal, and accelerometry. The bottom black bar shows when a stimulation was sent to awaken the animal (white bar). B. Percent time spent in PS per hour over the 12-hour baseline (in light blue 8h-14h and dark blue 14h-20h), PS deprivation period (from 8 to 14h in light green), and PS recovery period (from 14h to 20h in dark green). B. the histogram shows the mean quantity in minutes of PS during 6-hour baseline (left) compared to the remaining quantity of PS during PSD (right). C. The histogram illustrates the increase in PS after deprivation by showing the percentage of PS during 6 hours of baseline (left) compared to the 6 hours after the deprivation (right). D. the table compares the residual quantities of PS during PSD and PSR, during 4

  13. D). Characteristics of wakefulness and sleep obtained from 12h night recordings of a pigeon with the ONEIROS logger. A. Power spectrum in each state computed for right EEG. B. Distribution of the muscle tone per epoch for each state. C. Distribution of the Delta/Gamma ratio for each state. D. Percentage of time spent in each state during 12h night recordings. E. Mean episode duration for each state. AW -active wakefulness; QWquiet wakefulness; SWS -slow wave sleep; PS -paradoxical sleep. Accelerometry axis direction: x -forwardbackwards, y -lateral, z -vertical.

VI. ACKNOWLEDGEMENTS

We are grateful for the financial support of the Université de Lyon through the Program 'Investissement d'Avenir' (ANR-1 1-IDEX-0007) and the CNRS interdisciplinary mission through the PEPS EXOMOD 2015-2016 (PHYLOREM project).

Screen shot of the configuration tool design on Matlab for allocating the sampling rates to the different channels.

Biosignals

The 26 unipolar channels for biosignal measurements are acquired through the analog frontend which contains one instrument amplifier (referred to a reference electrode) for each channel with a fixed gain of 192 V/V. The output of all amplifiers can be multiplexed to the input of a single 16-bit analog to digital converter. These channels are meant to measure biosignals, such as EEG, ECG, EMG, EOG and LFP. Each channel has a

SUPPLEMENTARY VIDEO LEGENDS

Supplementary Video 1: the video illustrates the online paradoxical sleep deprivation on the rat. From the top to the bottom: the hypnogram (in red SWS, in green PS, in blue WK), the differential EMG, the EOG, the raw and time frequency representation of the parietal EEG. The video has been accelerated 2 times. In the top left corner a red rectangle appears when a stimulation was send.