Marie-Laetitia Pastor
email: marie.laetitia.pastor@iut-tarbes.fr.

Christian Garnier

Christophe Pescay

Comparison of two Nondestructive Tests in Carbon/Epoxy Composites

Keywords: Ultrasounds, infrared thermography, NDT, composites

An investigation of two nondestructive methods (ultrasounds and infrared thermography) is carried out. The defect detection by both methods is first examined. The determination of the position and the dimensions are studied. And finally, the feasibility and the time of the experimental protocol setting up are analysed. The aim is to compare two nondestructive methods: ultrasounds and infrared thermography applied to composites samples. So, three different specimens are tested. It appears that the majority of the defects is detected very quickly with infrared thermography compared to ultrasounds method. However, certain defects are not visible by infrared thermography.

Introduction 

Aeronautical composite components are subjected to extreme conditions which may cause in-depth defects such as cracks, delamination, and so on. For safety reasons, it is necessary to detect these defects in order to repair, to reinforce or to change the damaged parts [START_REF] Pastor | On the fatigue response of aluminium specimens reinforced with carbon-epoxy patches[END_REF]. So it is important to use nondestructive methods to detect them in a fast and easy way. The repair or the reinforcement of aeronautical structures can often be carried out on the plane. Thus, it is interesting to detect defects in-situ. Today, the only NDT method which enables the certification is the ultrasonic testing [START_REF] Burke | Nondestructive characterization of advanced composite materials[END_REF]. It enables easily and precisely to detect many defects such as delamination, disbanding, and so on [3]. Nevertheless, it is a very slow process. So, in order to stage with this disadvantage, since the last twenty years, optical methods, in particular IR thermography, are applied in non destructive tests. Thanks to thermography IR, it is possible to analyze structures on plane. In fact, many defects in composites are revealed by IR thermography such as impact damage, delamination, disbanding, and so on [START_REF] Hay | Techniques de l'ingénieur R 2 955 Traité mesures et contrôles[END_REF][START_REF] Pajani | Techniques de l'ingénieur R 2 740 Traité mesures et contrôles[END_REF].

In the present work, an investigation of two nondestructive methods (ultrasounds and thermography IR) is carried out. The first part of the study consists in detecting defects by two nondestructive methods. So, three specimens with different defects are tested. The aim of the second part is to determine the position and the dimensions of defects by ultrasounds and thermography IR. So the parameters obtained by both nondestructive methods are compared to the theoretical dimensions of defects. And finally, the setting up of experimental protocols and the test times are analyzed according to the applied method.

Materials

The specimens are carried out from 8 woven plies carbon/epoxy composite. Specimens are rectangular plates with a thickness almost equal to 2.8 mm. Different known defects are inserted in the specimens.

Density

Young's modulus (GPa) Specific heat

(J•kg -1 •K -1 ) Thermal conductivity (W•m -1 •K -1 )
Thermal expansion coefficient (10 -6 •K -1 ) Celerity (m•s -1 ) Composite 1.4-1.7 129-154 902-1037 3.9-6.6 -0.44 -0.16 2600-2700

Fig. 1 Geometries of the three tested specimens.

Properties

The use of infrared thermography and ultrasounds requires some mechanical and thermal properties which are mentioned in Table 1.

Geometries

Three specimens with different defects are tested (Fig. 1). A and B specimens have approximately the same dimensions. They are 150 mm long and 100 mm wide. However defects are different. The defect present in A specimen is a delamination located in the middle of the plate. This defect was experimentally produced. The defect in B specimen is a peel ply located between plies. This defect was intentionally inserted in the composite. So the form and the position of the defect are well known. Dimensions of C specimen are 170 mm long and 150 mm wide. Several identical defects are present in the composite. The position and the diameter of the defects are plotted in Fig. 2. The defects are Teflon coated fibre glass. These defects were intentionally inserted in the composite.

Ultrasounds

The ultrasounds method consists in analyzing the propagation of the high-frequency mechanical sound waves inside material [START_REF] Huang | Quality control and nondestructive tests in metal matrix composites[END_REF][START_REF] Birk | Nondestructive Testing Handbook[END_REF]. In fact, for high frequencies, the ultrasounds do not propagate in the air. The reflected sound waves enable to identify defects A specimen B specimen C specimen inside material. Thus the position, the depth and the shape of the defects can be determined.

Experimental Procedure

A transducer connected to a portable sound encoder called Omniscan enables to propagate the sound waves in the material and to capture the reflected waves (Fig. 3). In order to improve the propagation of the sound waves, a gel is applied to the materiel. The ultrasounds tests are performed at 5 MHz with celerity equal to 2600 m•s -1 . The first fast ultrasounds test is carried out to adjust the software parameters in order to detect the most precisely possible the defects in the tested

The operator moves the on all the surface of the specimen and looks at the reflected sound signals. For the tested specimen size, the ultrasounds tests last approximately 30 min.

Ultrasounds Results

The ultrasounds tests are highlighted defects in the three tested specimens (Fig. 4).

For A specimen, the delamination was not detected by ultrasounds on the face opposed to the impact. In fact, the fibres are pulled out. Air is thus present between fibres. Consequently the sound waves do no propagate. In Fig. 4a, the observed defect is the impact. The ultrasounds limit is reached. Nevertheless, dimension and localisation are determined. The defect is circular and the diameter is equal to around 22±0.25 mm. It is located 73±0.25 mm from the right side along the x axis and 50±0.25 mm from the bottom along the y axis.

For B specimen, the defect is located at 1±0.25 mm depth (Fig. 4b). Defect dimensions are 89±0.25 mm long (x axis) and 80±0.25 mm wide (y axis). It is locat- With ultrasounds, defect diameters are always overestimated compared to theoretical diameters inserted in the C specimen. The smaller is the defect diameter, the more important is the error. For the defects which are 4 mm in diameter, the error is equal to 100%. For the defects which are 15 mm in diameter, the error is equal to 13%. The errors can be explained by the dilatation of the images (Fig. 4c). The same remarks can be made for the defect position. However, the error value is a little weaker for the position along the y axis than the ones along the x axis. In this case, the defect size is not considered any more. Only the defect alignment is taken into account. The defects depth was also determined. However, it is very difficult to calculate the errors on the defects depth. In fact, the theoretical value of the depth is not well-known. The defects are located respectively at 2 plies, 4 plies and 6 plies. In theory, the plies are 0.25 mm in thickness but in practice, it is difficult to have exactly 0.25 mm. The plies thickness varies from 0.25 to 0.35 mm. This variation comes from the composite compaction during manufacture. Thus, only an interval allows evaluating the error on the defects depth. They vary from 9% to 30%. Nevertheless, it is interesting to remark that all the defects of C specimen were detected.

Infrared Thermography

Periodic heat energy, generated by halogen lamps, is transferred to the material [START_REF] Meola | Infrared thermography to detect residual ceramic in gas turbine blades[END_REF][10][11]. Thermal waves are propagated by conduction inside the material and are reflected on the boundaries. The wave reflection is disturbed by defects. The IR camera measures temperatures which are modified due to defects.

Experimental Setup

The temperature field is captured by an infrared CEDIP camera which features a focal plane array of 320×240 pixels. The thermal resolution of the camera is about 20 mK. Two halogen lamps are located at 300 mm from the specimen and heat it (Fig. 5). The

Thermography Results

Infrared thermography tests are highlighted defects in both specimens (Fig. 6). The peel ply was not detected by thermography IR. This is certainly due to the fact that the peel ply is well stuck to composite fibres. Thus, they constitute one and only one material and consequently it has the same conductivity as the resin. The experimental protocol was improved in order to try to detect the defect but that is not sufficient. So, this method presents some limits.

For A specimen, the delamination was detected by thermography IR very quickly (around 10 s) and very easily. After one test, the defect was detected (Fig. 6a). The specimen is heated on the same face as the temperature field is measured. Like with the ultrasounds method, the dimension and the localisation are determined. The defect is circular and the diameter is approximately equal to 12±2 mm. It is located 70±1 mm from the right side along the x axis and 41±1 mm from the bottom along the y axis. The measurements precision depends on the pixel size and the image quality. It was checked that the measurement method is reproducible by taking several measurements on a well-known specimen.

For C specimens, defects were detected by heating them on the back face and by measuring the temperature on the front face. With thermography IR, defects diameters are always underestimated compared to theoretical diameters inserted in the specimen. The major inconvenient with thermography IR is that the small diameters are not correctly detected. The defects are visible but the diameters determination is not exact. There is an error on the diameter size only for the smallest one. The error is equal to 50%. The defect position along y axis is determined at ±1 mm. They are the same ones as the theoretical values. Nevertheless, there is an error on the defect position along the x axis. The error is all the more important since the defect diameters are small. For defects which are 4 mm in diameter, the error is equal to 40%. For defects which are 15 mm in diameter, the error is equal to 3%. It is interesting to remark that all the defects of C specimen were detected.

Comparison

Both nondestructive methods enable to identify defects in different composite plates. Nevertheless, defects localisation and dimensions, experimental protocol setting up and experimental time differ from one method to another. Table 2 sums up the results obtained with both nondestructive methods.

The major advantage of the thermography is the experimental time. In 30 s, it is possible to know if the specimen or an industrial structure has a defect. However, according to the nature of the defects and/or the way in which they stick to adhesive or fibres, the thermography IR does not enable to detect them. It is important to note that for both nondestructive methods, the defects position is not exact. Nevertheless, with thermography IR, defect dimensions are correctly determined except for small defects. In fact, under 6 mm, defects are detected with difficulty. The advantage of ultrasounds is that the defects depth can x axis: overestimated y axis: ok at ±1 30 be evaluated. For the moment, it is not possible to determine the depth with thermography IR. But some works are in progress in this domain.

The inconvenient of ultrasounds is that in order to detect the more precisely possible the defects (location and dimension), it is necessary to sweep all the surface of the specimens. It is very long and tiresome. Moreover, it is important to adjust perfectly the Omniscan in order to determine defects dimension and position otherwise the effects edge can appear.

Conclusions

This paper examines two nondestructive methods: ultrasounds and thermography IR. The CND tests lead to the following conclusions:

1. Ultrasounds enable to detect all the defects whereas thermography IR does not detect a piece of peel ply in the composite.

2. Ultrasounds overestimate the defect diameter size.

3. With infrared thermography, defects under 6 mm are detected with difficulty.

4. The defect position is always overestimated with both methods.

5. The major difference between both nondestructive methods is the time to identify defects. Thermography IR is approximately 60 times faster than ultrasounds.

6. Further investigations are in progress in order to determine the defects depth by thermography IR and to carry out others CND tests on industrial specimens.

Fig. 2

 2 Fig. 2 Geometry of C specimen defects.

Fig. 3

 3 Fig. 3 Ultrasounds device.ed at the plate centre.With ultrasounds, defect diameters are always overestimated compared to theoretical diameters inserted in the C specimen. The smaller is the defect diameter, the more important is the error. For the defects which are 4 mm in diameter, the error is equal to 100%. For the defects which are 15 mm in diameter, the error is equal to 13%. The errors can be explained by the dilatation of the images (Fig.4c). The same remarks can be made for the defect position. However, the error value is a little weaker for the position along

Fig. 4

 4 Fig. 4 Defects in the three tested specimens.

Fig. 5

 5 Fig. 5 Thermography IR device.acquisition frequency of the films is equal to 50 Hz and the films last 30 s. The specimen is heated during 10 s

Fig. 6

 6 Fig. 6 Defects detected by thermography IR in two tested specimens.

Table 1 Mechanical and thermal properties in composite [6].

 1

Table 2 Comparison of ultrasounds and thermography results.

 2

	Methods	Defects	Defect detection Dimensions (mm)	Position (mm)	Experimental time (s)
	Ultrasounds Thermography delamination delamination peel ply Teflon coated fibre glass yes yes yes yes peel ply no	22±0.25 x axis: 89±0.25 y axis: 80±0.25 over-estimated 12 ±2	x axis: 73±0.25 y axis: 50±0.25 at the centre x axis: overestimated y axis: ok at ±0.25 x axis: 70 ±1 y axis: 41 ±1	1,200 1,200 1,800 10 30
		Teflon coated fibre glass yes small diameter excepted	Underestimated small diameter (4 mm) for

(a) A specimen (b) C specimen


Mahmoud Hassan

Ahmad Mheich

Fabrice Wendling

Olivier Dufor

Claude Berrou

Graph-based analysis of brain connectivity during spelling task

Keywords: High resolution EEG, Evoked potentials, Brain connectivity, Cognitive analysis, Graph theory. I

Most of the brain functions are based on interactions between neuronal assemblies distributed within and across distinct cerebral regions. A major challenge in neuroscience is to identify these networks from neuroimaging data. In this paper, we investigate the brain connectivity during a specific cognitive task: spelling the name of an object represented on a picture. By using high-resolution electroencephalography (hr-EEG) and phase synchrony analysis combined with graph theory-based analysis, we show that the topographic distribution of the phase synchrony and the graph parameters are very powerful tools to disclose the activated macro-regions involved in such a complex task.

INTRODUCTION

Brain connectivity is at the heart of many studies in neurosciences, either in the field of cognitive [START_REF] Stevens | The developmental cognitive neuroscience of functional connectivity[END_REF] or clinical [START_REF] Uhlhaas | Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology[END_REF] research. The main idea is that networks of interconnected distinct brain regions underlie normal brain functions (involved in learning, memory, behaviour adaptation to stimuli, emotions) as well as pathological processes in some brain disorders like epilepsy, autism or schizophrenia. Generally speaking, the terminology "brain connectivity" refers to three different concepts: i) "structural connectivity" which denotes anatomical links, ii) "functional connectivity" which rely on statistical dependencies between signals from different brain areas and iii) "effective connectivity" which introduces causal interactions among these signals [START_REF] Sporns | Networks of the Brain[END_REF].

Recently, the quantitative description of the topological organization of brain networks based on graph theory has received an increasing interest [START_REF] Rubinov | Complex network measures of brain connectivity: uses and interpretations[END_REF][START_REF] Hagmann | Mapping the structural core of human cerebral cortex[END_REF][START_REF] Meunier | Hierarchical modularity in human brain functional networks[END_REF]. An original theory socalled the "mental information theory" was developed by Berrou et al. with the aim of designing new neuro-inspired intelligent machines [START_REF] Berrou | Graphs, codes and the brain[END_REF][START_REF] Berrou | Petite mathématique du cerveau. Une théorie de l'information mentale[END_REF].

Briefly, it considers any cortical network as an informational model of recurrent graph where each node is not a single neuron, but a group of neurons, called a microcolumn. These microcolumns can merge to form columns (clusters of neurons) that in turn, form macrocolumns together. In this model, mental pieces of information, called "infons", are carried by cliques, that is, small combinations of microcolumns that activate together. The clique can be viewed as a sub-graph in the vast network of microcolumns present in the human neocortex. By introducing some properties of redundant coding and parcimony in the recurrent graph model, authors showed theoretically how neural cliques are forming during a cognitive process. Further to the theory, we assume that the activation of brain networks at the "micro-level" i.e. clique will lead to the activation of brain networks at the "macro-level" i.e. the brain surface. Thus, the purpose of the present paper is to detect the activated "macro-networks" using hr-EEG recorded during a spelling task.

II. MATERIAL AND METHODS

A. EEG recordings and pre-processing

Nine subjects were shown pictures (n = 148) and were asked to spell the name of the object displayed on them. The 148 images are selected from the database of 400 pictures standardized for French [START_REF] Alario | A set of 400 pictures standardized for French: Norms for name agreement, image agreement, familiarity, visual complexity, image variability, and age of acquisition[END_REF] and is divided into two sessions (eight minutes each) of 74 stimuli. The brain activity was acquired using high resolution EEG recording system (hr-EEG, EGI Electrical Geodesic Inc.) allowing for the positioning of 256 electrodes on the head of the participant. The unique feature of this system is the "almost full" coverage of the subject's head by surface electrodes. This increased number of electrodes offers the possibility to better estimate the overall activity of the neocortex from non-invasive scalp measurements. Moreover, the temporal resolution of the recorded signals is excellent (in the order of 1 ms) as EEG signals are collected with a 1 kHz sampling frequency and filtered at 3-45Hz. An averaging process is done on stimulus-aligned epoch of 920 ms starting from the moment the picture appeared on screen (200 ms are added to the analysis as pre-stimulus considered as baseline).

Each trial was visually inspected, and epochs contaminated by eye blinking, movements or other noise were rejected and excluded from the analysis.

B. Phase synchony analysis

Several methods have been proposed to analyse the statistical coupling between signals. These methods can be categorized in three families: linear and nonlinear regression, phase synchronisation and general synchronisation (see [START_REF] Wendling | From EEG signals to brain connectivity: a model-based evaluation of interdependence measures[END_REF] for review). As a first step and given that we are dealing with short signal (600 to 900 ms), a good candidate method appears to be the phase synchronisation (PS) method which has been widely used in the field of ERPs brain connectivity [START_REF] Rodriguez | Perception's shadow: long-distance synchronization of human brain activity[END_REF]. The general principle of the PS method is to detect the existence of a phase locking between two systems defined as: In this paper we are using the method based on Hilbert transform as it has recently been shown that the application of both approaches (i.e., HT and wavelet transform) produces essentially the same result [START_REF] Quian Quiroga | Performance of different synchronization measures in real data: a case study on electroencephalographic signals[END_REF]. The second step is the definition of an appropriate index to measure the degree of synchronization between estimated instantaneous phases. We are using the phase locking value (PLV) defined as:

( , ) ( , ) ( , ) xy t n t n t n C       where Φ x (t;n), Φ y (t,
( , )

1 1 N i t n t n PLV e N    
PLV is a normalized measure varying between 0: random phase difference and 1: constant phase difference. To illustrate the connectivity in predefined time windows, the average PLVs in each window are computed.

C. Graph analysis

Using a graph theory based analysis, we extracted parameters from the connectivity graphs computed using the PLV in order to characterize these graphs at the different instant (from the Onset to the motor response). Each electrode is considered as a node and the significant PLVs are representing the edges. In this paper we focus on the difference between left and right hemisphere. The main parameters extracted from both sides are the following: -

The degree: The number of links connected to a node. -The Number of Edges (NE): The number of links in the network.

-The Betweenness Centrality (BC): The ratio between the number of shortest paths passing through a specific node and the total number of shortest paths in the network. -The density (D): The fraction of present connections to all possible connections of a network. -The lateralization Index (LI): compute the difference between left and right hemisphere and defined as:

( ) - ( ) ( ) *100 ( ) ( ) BC Right BC Left LI BC BC Right BC Left     
with LI being negative (-100 to 0) or positive (0 to 100) when BC is more prominent over the left hemisphere or the right hemisphere respectively. Note that we are only considering the values intra-hemisphere while the inter-hemisphere connections are ignored.

The analyzed graphs here are considered as unweighted and undirected. For more details about these parameters and others, see [START_REF] Rubinov | Complex network measures of brain connectivity: uses and interpretations[END_REF][START_REF] Costa | Characterization of complex networks: A survey of measurements[END_REF].

III. RESULTS

A. ERP analysis

The Fig. 1 shows the ERPs averaged over all subjects and trials. The signal length is chosen as the minimal value of all the reaction times for the sessions. We could clearly delineate several potentials like the positivity 150 (P150) which reflects the activity in early visual extrastriate cortex that is involved in the initial phase of visual feature processing and which is sensitive to changes in those elementary features whatever the object is: a picture [START_REF] Eddy | Masked repetition priming and event-related brain potentials: A new approach for tracking the time-course of object perception[END_REF] or a letter drawing [START_REF] Petit | On the time course of letter perception: A masked priming ERP investigation[END_REF]. In accordance with a large number of behavioral studies, using pictures and words, we also detected a negativity around 200 ms which reflects the access to the semantic of the picture; a process known to take place before the phonological one [START_REF] Schmitt | Electrophysiological estimates of semantic and syntactic information access during tacit picture naming and listening to words[END_REF]. Finally, we also observed the P3a et P3b potentials whose properties reveal the link between the intentional system and the ongoing process [START_REF] Comerchero | P3a and P3b from typical auditory and visual stimuli[END_REF].

Figure1: The average ERP signals over subjects and trials.

B. Brain connectivity

An example of the spatiotemporal distribution of phase synchrony from 200 ms before the stimulation onset (0 ms) to motor response (>720 ms) is presented in Figure 2. A classical symmetric montage of 32 electrodes is used for visualization simplicity. The lines represent the significant phase synchronization values between pairs of electrodes (compared to shuffled data with p<0.01, see [START_REF] Rodriguez | Perception's shadow: long-distance synchronization of human brain activity[END_REF]). It can be observed that, immediately after the onsets, within a period that grossly correspond to the visual processing (0-180ms), synchronies are present in left parieto-occipital and fronto-temporal regions. The graph parameters BC, NE and D (summarized in table I) are clearly higher in the left side of the brain with 0.022, 8 and 0.087 against 0, 0 and 0 respectively. Consequently, the lateral index LI equals to -100 (full left lateralization). A remarkable increase in the phase synchrony is then observed during the next 180ms (180-360 ms) and located essentially at the left occipital, frontal and temporal regions which are supposed to be involved at this stage while recovering the names of the images [START_REF] Price | The neural regions sustaining object recognition and naming[END_REF]. BC, NE and D (0.12, 16 and 0.175) at the left and (0, 0 and 0) at the right with LI= -100 confirm this lateralization. Also the degrees of the different node show that the channel at the left central parietal, left frontal parietal and left temporal (and frontal temporal) have the highest degrees (number of connected links) which reflect the channels (and the regions) of interest supposed to be active during this task stage. The phase synchronization between 360ms and 720ms present a more spatial distribution of the connectivity in both hemispheres with always a superiority of the left side (BC=0.06, NE=18 and D=0.197)) on the right side (BC=0.003, NE=3 and D=0.03) with LI= -88.5.

Finally, a phase desynchronization occurs and a diminution of every indices is observed at the temporal and parietal sites while the motor response is given (720-900 ms) (left: BC=0.001, NE=5, D=0.05 and right: BC=0.0009, NE=2 and D=0.02) with LI= -33.3). These results corroborate those reported in the neuroimaging literature, as processes implicated in the spelling network are predominantly located in the left hemisphere [START_REF] Price | The neural regions sustaining object recognition and naming[END_REF][START_REF] Purcell | Examining the central and peripheral processes of written word production through meta-analysis[END_REF]. However, ones need to delineate more "cognitively" the different period of time the brain goes through.

IV. DISCUSSIONS

Preliminary results were presented to demonstrate that hr-EEG can be used to reveal the cortical network(s) involved in a specific cognitive task. These observations are promising regarding the accordance with the state of the art on language processing and the complexity of the task which, among several processes, involves the working memory system at stake in the mental information theory [START_REF] Hagmann | Mapping the structural core of human cerebral cortex[END_REF][START_REF] Meunier | Hierarchical modularity in human brain functional networks[END_REF].

The connectivity graphs presented here showed the brain networks behavior at different stage: from visual processing to motor response. The time window used for computing the graph of brain connectivity is chosen based on the state of the art regarding similar cognitive task. We are working on more optimal choice by using a method referred to a spatiotemporal segmentation of the ERPs [START_REF] Perret | Comparison of electrophysiological correlates of writing and speaking: A topographic ERP analysis[END_REF].

We highlight that the results do not represent the "cliques" as they are defined in the mental information theory. However, we consider these results as an excellent preliminary step.

Our ongoing work is focusing on analyzing the network model of brain connectivity referred to the graph theory based analysis at the source level by passing from electrode level to the "source" level using inverse methods (MUSIC) to get closer to "neural cliques". More graph parameters will be explored specially those offering information about the presence of sub-networks inside the whole complex network, such as the "modularity" and "community" parameters.

V. CONCLUSION

In this paper, we presented a study aiming to identify the cortical regions involved during a spelling task. The use of the hr-EEG and the connectivity analysis (phase locking value) indicated clearly that the left hemisphere is much more implicated during the spelling at the different instant than the right hemisphere. The basic graph parameters (betweenness centrality, density, number of edges and the lateralization index) have showed high capacity to quantify this lateralization.

  n) are the unwrapped phases of the signals (x and y) representative of the two systems at time bins t, trial n [1, …, N] and C a constant. The first step for estimating the phase synchronization is to extract the instantaneous phase of each signal. Two different techniques can be used: the Hilbert transform and the wavelet transform.

Figure 2

 2 Figure 2 Topographic distributions of the phase locking values (PLV) during the spelling task for a representative subject (0 represent the stimulus instant).

Table I :

 I Graph parameters extracted from left and right hemisphere

			0:180ms	180:360 ms	360:540 ms	540:720 ms	720:900 ms
		BC	0.022	0.12	0.107	0.06	0.001
	Left	NE	8	16	18	18	5
		D	0.0879	0.175	0.197	0.197	0.05
		BC	0	0	0	0.037	0.0009
	Right	NE	0	0	1	3	2
		D	0	0	0.011	0.033	0.022
		LI	-100	-100	-100	-88.57	-33.3

This work has received a French government support granted to the CominLabs excellence laboratory and managed by the National Research Agency in the "Investing for the Future" program under reference ANR-10-LABX-07-01.

This work is supported and partly granted by the Committee for Clinical Research (COREC) at the CHU of Rennes.