David J White

Marco Congedo

Joseph Ciorciari

Richard B Silbertein

Brain Oscillatory Activity during Spatial Navigation: Theta and Gamma Activity Link Medial Temporal and Parietal Regions

Brain oscillatory correlates of spatial navigation were investigated using blind source separation (BSS) and standardized low resolution electromagnetic tomography (sLORETA) analyses of 62-channel EEG recordings. Twenty-five participants were instructed to navigate to distinct landmark buildings in a previously learned virtual reality town environment. Data from periods of navigation between landmarks were subject to BSS analyses to obtain source components. Two of these cortical sources were found to exhibit significant spectral power differences during navigation with respect to a resting eyes open condition and were subject to source localization using sLORETA. These two sources were localized as a right parietal component with gamma activation and a right medial-temporal-parietal component with activation in theta and gamma bandwidths. The parietal gamma activity was thought to reflect visuospatial processing associated with the task. The medial-temporal-parietal activity was thought to be more specific to the navigational processing, representing the integration of ego-and allo-centric representations of space required for successful navigation, suggesting theta and gamma oscillations may have a role in integrating information from parietal and medialtemporal regions. Theta activity on this medial-temporal-parietal source was positively correlated with more efficient navigation performance. Results are discussed in light of the depth and proposed closed field structure of the hippocampus and potential implications for scalp EEG data. The findings of the present study suggest that appropriate BSS methods are ideally suited to minimizing the effects of volume conduction in non invasive recordings, allowing more accurate exploration of deep brain processes.

Introduction

Medial temporal lobe (MTL) structures, the hippocampus in particular, have been implicated in the process of spatial navigation. After identifying 'place cells' in the rodent hippocampus which fired based on position in the environment, O'Keefe and colleagues postulated a cognitive map of the environment stored within the hippocampus (O' Keefe & Nadel, 1978). Single-unit recordings have since demonstrated the presence of hippocampal cells with similar properties in primates [START_REF] Hori | Place-related neural responses in the monkey hippocampal formation in a virtual space[END_REF][START_REF] Matsumura | Spatialand task-dependent neuronal responses during real and virtual translocation in the monkey hippocampal formation[END_REF] and humans [START_REF] Ekstrom | Cellular networks underlying human spatial navigation[END_REF]. Hippocampal place cells interact with cells from surrounding structures including grid cells of the entorhinal cortex [START_REF] Hafting | Microstructure of a spatial map in the entorhinal cortex[END_REF] to provide a representation of space [START_REF] Moser | Place cells, grid cells, and the brain's spatial representation system[END_REF]. Functional neuroimaging has enabled exploration of brain processes involved in human spatial navigation, using virtual reality (VR) tasks to simulate navigation through realistic three-dimensional environments. This research has consistently shown increased activity in the right MTL, as well as parietal regions, among a network of active areas during navigation [START_REF] Grön | Brain activation during human navigation: Gender-different neural networks as substrate of performance[END_REF][START_REF] Hartley | The well-worn route and the path less traveled: Distinct neural bases of route following and wayfinding in humans[END_REF][START_REF] Iaria | Retrosplenial and hippocampal brain regions in human navigation: Complimentary functional contributions to the formation and use of cognitive maps[END_REF][START_REF] Maguire | Knowing where and getting there: A human navigation network[END_REF]. Further, neuropsychological testing of humans with damage to the hippocampus, and surrounding MTL has demonstrated a number of spatial processing and memory deficits [START_REF] Astur | Humans with hippocampus damage display severe spatial memory impairments in a virtual Morris water maze[END_REF][START_REF] Bohbot | Spatial memory deficits in patients with lesions to the right hippocampus and to the right parahippocampal cortex[END_REF][START_REF] Lee | Specialization in the medial temporal lobe for processing of objects and scenes[END_REF][START_REF] Nunn | Differential spatial memory impairment after right temporal lobectomy demonstrated using temporal titration[END_REF][START_REF] Parslow | Allocentric spatial memory in humans with hippocampal lesions[END_REF].

Recent models of spatial processing have integrated findings from rodent research, functional neuroimaging and neuropsychological deficits in humans, working to highlight a role for MTL and parietal regions. The hippocampus and surrounding MTL structures are thought to be important for long-term allocentric (world-centred) representations of the environment, whilst the parietal lobe has been proposed as providing short-term egocentric (self-centred) representations within the environment [START_REF] Burgess | Spatial cognition and the brain[END_REF][START_REF] Byrne | Remembering the past and imagining the future: A neural model of spatial memory and imagery[END_REF][START_REF] Whitlock | Navigating from hippocampus to parietal cortex[END_REF]. In reviewing recent progress in the understanding of the MTL in spatial processing, [START_REF] Moser | Place cells, grid cells, and the brain's spatial representation system[END_REF] highlight the gap in understanding as to how spatial representations of the MTL interact with other regions, such as the parietal lobe, also important for spatial navigation. In line with rodent findings, [START_REF] Byrne | Remembering the past and imagining the future: A neural model of spatial memory and imagery[END_REF] propose the theta rhythm is ideally suited to facilitate communication between MTL and parietal regions. However the importance of gamma, as well as theta, oscillations in long-term representations of space in the rodent hippocampus has been demonstrated (for a review, see [START_REF] Lisman | The theta/gamma discrete phase code occurring during the hippocampal phase precession may be a more general brain coding scheme[END_REF]. Further, recordings from rodent hippocampus and neocortex suggest neocortical gamma phase is modulated by hippocampal theta [START_REF] Sirota | Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm[END_REF], and theta/gamma coupling has been proposed as a neural coding system beyond the hippocampus [START_REF] Lisman | The theta/gamma discrete phase code occurring during the hippocampal phase precession may be a more general brain coding scheme[END_REF][START_REF] Lisman | A neural coding scheme formed by the combined function of gamma and theta oscillations[END_REF]. From this, one may speculate that theta and gamma activity facilitate the information transfer of different spatial representations required for successful navigation.

Much of the research exploring oscillatory correlates of human spatial navigation has utilized intracranial EEG (iEEG) recordings, due to the difficulties in recording activity from the implicated deep medial temporal regions. Using iEEG recordings from epileptic patients during VR maze navigation, task dependent theta bursts have been identified in subdural grid electrodes in neocortex [START_REF] Caplan | Distinct patterns of brain oscillations underlie to basic parameters of human maze learning[END_REF][START_REF] Kahana | Human theta oscillations exhibit task dependence during virtual maze navigation[END_REF]. Expanding on these findings, [START_REF] Caplan | Human theta oscillations related to sensorimotor integration and spatial learning[END_REF] found navigation-related theta activity across cortical sites during VR town navigation, which was later shown to covary with navigation-related hippocampal theta [START_REF] Ekstrom | Human hippocampal theta activity during virtual navigation[END_REF]. While supporting the hypothesized role for theta integrating allocentric information from the hippocampus with other cortical regions required for successful navigation, work from this research group has also highlighted navigation-related activity in numerous frequency rangesincluding gamma oscillations [START_REF] Caplan | Distinct patterns of brain oscillations underlie to basic parameters of human maze learning[END_REF][START_REF] Ekstrom | Human hippocampal theta activity during virtual navigation[END_REF]Jacobs et al., 2009).

Non-invasive techniques such as EEG and MEG provide an opportunity to explore this relationship in larger samples of healthy volunteers. EEG studies of maze navigation have reported increased theta power [START_REF] Bischof | Spatial navigation in virtual reality environments: An EEG analysis[END_REF], and increased theta coherence between right parietal and temporal regions [START_REF] Mackay | Human theta oscillations in virtual maze navigation and Sternberg tasks[END_REF]. However, such analyses do not facilitate any real conclusions as to the involvement of the MTL in spatial navigation. Complicating matters further, the hippocampus has traditionally been viewed as a closed field structure (eg. [START_REF] Fernández | Real-time tracking of memory formation in the human rhinal cortex and hippocampus[END_REF] -isopotentially zero external to the hippocampus proper due to its cellular architecture (Lorente de Nò, 1947) -making any inferences as to hippocampal activity from non-invasive recordings difficult. Whilst some reports have challenged the notion of the hippocampus as a closed field structure; anatomically and electrophysiologically realistic modelling of the hippocampus and other deep structures suggests non-invasive EEG and MEG may in fact be capable of localizing such activity [START_REF] Attal | Modelling and detecting deep brain activity with MEG and EEG[END_REF], and hippocampal theta has been reported using MEG [START_REF] Tesche | Theta oscillations index human hippocampal activation during a working memory task[END_REF], direct evidence is lacking which would allow firm conclusions as to hippocampal contribution to surface recorded EEG. Regardless, it has been proposed that task induced scalp theta activity may provide an indirect 'window' with which to view cortico-hippocampal communication [START_REF] Bastiaansen | Event-induced theta responses as a window on the dynamics of memory[END_REF].

Elevated theta power associated with VR navigation has been demonstrated using MEG [START_REF] Cornwell | Human hippocampal and parahippocampal theta during goal-directed spatial navigation predicts performance on a virtual Morris water maze[END_REF][START_REF] De Araújo | Theta oscillations and human navigation: A magnetoencephalography study[END_REF]. Using a VR town navigation task, a strong increase in theta power during periods of navigation was localized to temporal and parietal regions using a single current dipole model; the authors concluding that more sophisticated source modelling could provide more definitive evidence of medial temporal involvement [START_REF] De Araújo | Theta oscillations and human navigation: A magnetoencephalography study[END_REF]. Using a beamformer spatial filtering technique, elevated theta activity during navigation using a modified VR Morris water maze was observed in left hippocampal and parahippocampal regions [START_REF] Cornwell | Human hippocampal and parahippocampal theta during goal-directed spatial navigation predicts performance on a virtual Morris water maze[END_REF]. This left medial temporal lobe activity was found to correlate with successful navigation. Subsequent exploratory analysis of the whole brain volume revealed theta sources were largely confined to left temporal and frontal regions. The use of an 'aimless movement' control condition may have understated the role of other important parts of the navigation network, given previous reports of virtual movement suggest the presence of 'movement-related theta' [START_REF] Ekstrom | Human hippocampal theta activity during virtual navigation[END_REF]. If a significant portion of the network is active during any virtual movement, the use of such a condition for contrast may explain why theta source activity was only observed in left medial temporal regions, in contrast with much of the navigation literature showing either bilateral or right lateralised involvement.

Given that the magnetic field of a deep dipolar source will fall off at a rate greater than the equivalent electric field [START_REF] Nunez | Electric Fields of the Brain: The Neurophysics of EEG[END_REF], EEG may represent a more natural candidate for the study of deep regions such as the medial-temporal lobe.

Still, deep sources represent a challenging scenario for EEG source localization techniques, as observed EEG is the sum of activity from widespread brain sources (plus artifacts) as a result of volume conduction [START_REF] Nunez | Electric Fields of the Brain: The Neurophysics of EEG[END_REF], and superficial or strong sources make deeper or weaker sources difficult to localize with popular source localization techniques such as standardized Low Resolution Electromagnetic Tomography (sLORETA; see for example [START_REF] Wagner | Evaluation of sLORETA in the presence of noise and multiple sources[END_REF]. Recently, data independent and data driven spatial filters have been applied to EEG data as a means of minimising the effects of volume conduction, thus enhancing the sensitivity of source localization techniques [START_REF] Congedo | Subspace projection filters for real-time brain electromagnetic imaging[END_REF][START_REF] Onton | Imaging human EEG dynamics using independent component analysis[END_REF].

Blind Source Separation (BSS) is a group of processing techniques which seek to identify source activity from a mixed signal, and has been employed in a variety of fields including speech processing [START_REF] Jang | Learning statistically efficient features for speaker recognition[END_REF], face recognition [START_REF] Yuen | Face representation using independent component analysis[END_REF]Lai, 2002), wireless communication (van der Veen et al., 1997), radar applications [START_REF] Fiori | Overview of independent component analysis technique with an application to synthetic aperture radar (SAR) imagery processing[END_REF], and with a range of biomedical signals [START_REF] James | Independent component analysis for biomedical signals[END_REF]. In EEG data BSS has been used for the identification and removal of artifacts (Delorme et al., 2007a;[START_REF] Romero | A comparative study of automatic techniques for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: A simulation case[END_REF] and to provide source identification prior to a source localization step [START_REF] Mutihac | A comparative study of independent component analysis algorithms for electroencephalography[END_REF]. After decomposing the observed EEG into a number of elementary additive sources subsequent source localization is more reliable, typically capable of exact localization of single equivalent dipoles. BSS techniques have been used to enhance source localization efforts with ERP data [START_REF] Marco-Pallarés | Combined ICA-LORETA analysis of mismatch negativity[END_REF][START_REF] Grau | Functional neural dynamics underlying auditory event-related N1 and N1 suppression response[END_REF], ictal EEG in epilepsy [START_REF] Leal | Analysis of the generators of epileptic activity in early-onset childhood benign occipital lobe epilepsy[END_REF][START_REF] Burgess | Spatial cognition and the brain[END_REF], and task-related time-frequency domain EEG [START_REF] Onton | Frontal midline EEG dynamics during working memory[END_REF]Delorme et al., 2007b;[START_REF] Huang | Tonic and phasic electroencephalographic dynamics during continuous compensatory tracking[END_REF].

Several BSS methods, including independent component analysis (ICA), exist.

Based on physiological and statistical consideration the Approximate Joint Diagonalization of Fourier Cospectral matrices (AJDC) has been suggested as a method well suited to EEG data [START_REF] Congedo | On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics[END_REF]. In its basic form AJDC seeks a linear transformation of the data jointly diagonalizing Fourier cospectral matrices at multiple frequencies. As these matrices hold in the off-diagonal part the in-phase covariance of all pair-wise EEG sensors, their diagonalization produces spatial filters extracting uncorrelated sources. Since the diagonalization set includes several frequencies, one is assured to extract source components as long as they have non-proportional powerspectra, whereas each isolated component may involve either focal or distributed regions oscillating synchronously (in-phase with same or opposite sign) at the same frequencies.

Using this AJDC approach components obtained from scalp EEG recordings have demonstrated high correlations with simultaneously recorded subdural iEEG in a study on tinnitus (van der Loo et al., 2007). More recently, the AJDC method has been extended to group data analysis to obtain a common set of filters describing the sample under study [START_REF] Congedo | Group independent component analysis of resting-state EEG in large normative samples[END_REF].

The aim of the present work was to pursue this line of inquiry, exploring the generators of brain oscillatory activity in spatial navigation, using group-BSS to identify sources which could then be subject to source localization methods. Based on recent models of spatial cognition, and research using iEEG and MEG, it was hypothesized that navigation would be associated with increased medial temporal lobe and parietal lobe activity, particularly in the theta frequency band. The novelty of the present study in the context of the existing literature on spatial navigation resides in the use of a group-BSS method applied to scalp EEG to visualize MTL activity.

Methods

Participants

The sample consisted of 25 healthy right-handed volunteers with normal or corrected-to-normal vision and no history of head injury, epilepsy or psychiatric diagnoses (13 Female (M = 24.62, SD = 4.84); 12 Male (M = 24.75, SD = 4.83)). All participants reported experience with computer and video games, the majority rating themselves as 'occasional users'. Written informed consent was obtained from all participants, in accordance with the Swinburne University Research Ethics Committee.

EEG Acquisition

EEG activity was recorded using an electrode cap with 62 scalp electrodes positioned according to the extended 10-10 system (Quik-Caps; Neuro Scan Inc., Abbotsford, VIC, AUS). On-line recordings were referenced to an electrode positioned between Cz and Cpz, with a ground electrode between Fz and Fpz. In order to monitor eye movement and blinks, additional electrodes were placed on the infraorbital and supraorbital regions of the left eye, as well as a bipolar channel located at the outer canthi of each eye. Recordings were also taken from both left and right mastoids, to facilitate a linked mastoid reference in off-line analysis. Electrode impedance at all sites was maintained below 5kΩ. EEG recordings were digitized at 500Hz with a band-pass filter between 0.1Hz and 70Hz and a notch filter at 50Hz (single-pole Butterworth, 6dB down at cut-offs) using the SynAmps2 amplifier system and Scan 4.3 Acquisition software (Neuroscan, Inc.).

Procedure

Participants were seated 60cm from a 17 inch LCD monitor in a dimly lit room.

Baseline eyes open (2.5 minutes, fixation on centre of blank screen) and eyes closed (2.5 minutes) recordings were obtained, followed by completion of an EOG calibration task comprising a series of blinks, and left, right, up and down eye movements (calibration data was not utilized for this study). At this time, participants were given 10 minutes to learn the layout of the virtual reality town where no active instructions or buttons were present within the town (see town description below). Participants were instructed to try and form a mental representation of the layout of the town, paying particular attention to the landmark buildings. Pilot testing indicated 10 minutes was sufficient time to successfully learn the layout of the town (pilot participants were allowed to navigate through the town for as long as desired, until they felt they knew the location of all landmarks, after which time they were given a generic topographic map with the same street and building layout as the town, only with blank buildings. Participants were required to indicate where each of the landmarks were located; 10 minutes ensured correct identification of landmark locations). After the learning period, an unrelated simplified navigation task was carried out where subjects were required to move themselves within a small virtual room so as to face a particular orientation with respect to a central landmark (used in [START_REF] Seixas | Spatial coordinate systems within the parietal lobes: Localization of a cognitive spatial map in humans[END_REF]. The primary purpose of this period was to provide a delay of at least 5-7 minutes between learning the town layout and beginning the navigation task. Recordings were then made whilst participants completed the navigation task.

Virtual town navigation

Using the high resolution adaptation of Duke Nukem 3D (Duke Nukem 3D: 3D 1a). Participants navigated from a 3D first-person (see Figure 1b) view using the four arrow keys on a standard computer keyboard, and used the space bar to press a button located at each landmark. At the beginning of the navigation condition, participants were required to push a button at their start location, at which time instructions appeared to navigate to one of the landmark buildings. Upon arrival at that landmark, they were again required to find and push a button located on the landmark, then follow further instructions to navigate to another landmark building. Participants navigated to each of the six landmark buildings once, pushing a button located on the target building which provided instructions to move onto another landmark. Navigation task completion was recorded and reviewed to quantify were down-sampled to 128 Hz using a natural cubic spline interpolation routine [START_REF] Congedo | Notes on EEG resampling by Natural Cubic Spline interpolation[END_REF]. Both segments of navigation and baseline data, including the 62 EEG electrodes as well as the two vertical EOG channels were concatenated into a single file for each participant.

For each participant the data was then decomposed using AJDC [START_REF] Congedo | On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics[END_REF], whereupon the most energetic EOG component and EMG component were identified and removed from the data by linear filtering using ICoN software1 . The AJDC algorithm consisted of estimating 1-Hz resolution Fourier cospectral matrices [START_REF] Bloomfield | Fourier Analysis of Time Series[END_REF]p. 207) by Fast Fourier Transform (FFT) averaging over 50%

overlapping 1-second epochs as per the well-established Welch method [START_REF] Welch | The use of Fast Fourier Transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms[END_REF]. A

Welch tapering window was multiplied to each epoch to reduce side-lobe effects in FFT estimations. Then, the approximate joint diagonalization (AJD) for average cospectra in the range 1 to 28 Hz was obtained using the algorithm proposed by [START_REF] Tichavský | Fast approximate joint diagonalization incorporating weight matrices[END_REF]. Following our previous studies, this upper limit for the diagonalization set (28 Hz) followed the analysis of grand-average cospectral structures. For frequencies above 28 Hz it was verified in our data that the cospectral matrices become more and more diagonal, indicating a dropping of the signal-to-noise ratio. Thus, limiting the AJD to lower frequency effectively allows a more noise-resistant estimation of the spatial filters. Nonetheless, such spatial filters apply equally well to all frequencies due to the assumption of a linear instantaneous mixing model [START_REF] Congedo | On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics[END_REF]2010).

Group Analysis

Group BSS analysis followed four steps: (a) source identification using BSS, (b) testing for sources demonstrating spectral power differences from baseline to navigation, (c) source localization of those source components demonstrating spectral power changes, (d) correlating spectral power with navigation performance for those sources and bands showing significant task-related activity. The group-AJDC algorithm diagonalizes the grand average cospectral matrices. Since we are interested in brain activity related to spatial navigation, the grand-average (N=25) was computed on navigation data only. The AJDC algorithm specifications were as previous, but in this case a subspace reduction by truncated whitening limited the estimated components to 36, allowing more than 99% of explained variance to be preserved in the solution (Mean Square Representation Error = 0.0093). Such a subspace reduction strategy improves the signal-to-noise ratio while preserving the data of interest and makes the ensuing analysis more easily interpretable.

For each of the group source components obtained from the navigation condition, log transformed spectral power in the range 1-44 Hz was computed in 1-Hz frequency bins for both navigation and baseline condition, for each subject separately. Finally, a rank-transformed permutation t-max test [START_REF] Westfall | Resampling-based multiple testing[END_REF] was used to determine frequency bands where navigation and baseline source power significantly differed (30k random permutations). This method automatically corrects for the number of simultaneously tested null hypotheses (multiple comparisons) along frequencies within each component, whilst a Bonferroni adjusted significance threshold of p=0.00139 was used to control for multiple comparisons across the 36 components (0.05/36). Source components displaying significant results were localized in cortical space by applying the sLORETA inverse solution, after centering (referencing to the common average, which is necessary for sLORETA estimations; [START_REF] Congedo | On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics[END_REF], the corresponding column of the estimated BSS mixing matrix. For the sLORETA inverse solution (Pascual-Marqui, 2002) the regularization parameter (Tikhonov regularization, see [START_REF] Congedo | Subspace projection filters for real-time brain electromagnetic imaging[END_REF]) was set to 10 3 . We used the head model of the Key-Institute for Brain-Mind Research (Zurich, Switzerland), which incorporates a 3-shell spherical head model co-registered to the anatomical brain atlas of [START_REF] Talairach | Co-planar stereotaxic atlas of the human brain: Three-dimensional proportional system[END_REF], and makes use of standard EEG electrode coordinates derived from cross-registration between spherical and realistic head geometry [START_REF] Towle | The spatial location of EEG electrodes: Locating the best fitting sphere relative to cortical anatomy[END_REF]. The solution space is restricted to cortical gray matter using the digitized probability atlas of the Brain Imaging Center at the Montreal Neurological Institute [START_REF] Collins | Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space[END_REF], divided in 2394 voxels measuring 7 x 7 x 7 mm.

Spectral power estimates for scalp electrode sites were obtained from the same navigation and resting eyes open datasets used in the group BSS analysis. These spectral estimates followed the same procedure as that used to derive spectral estimates used as part of the group-BSS analysis. The same rank-transformed permutation t-max test [START_REF] Westfall | Resampling-based multiple testing[END_REF] was used to determine frequency bands where navigation and baseline scalp electrode spectral power significantly differed. This analysis used the same significance testing strategy as for the group BSS sources, where the t-max test controls for multiple comparisons across frequencies, and Bonferroni correction was used to control for the comparisons across all electrodes (0.05/62 = 0.0008). Scalp maps of tscores contrasting grand average power spectra during navigation versus eyes open baseline condition were also generated to explore those electrode locations where power in the theta band increased beyond baseline levels.

Results

Task performance was quantified as the summed latency (in seconds) from initiation of each block of navigation to arrival at each landmark. Whilst the task was designed to be relatively easy, variable performance was apparent across participants (mean latency = 151.64 seconds, SD = 11.67). As walking speed within the task was uniform, greater latency reflects greater distance travelled in order to reach landmarks, and thus less efficient navigation. This performance could then be correlated with those measures of brain activity which show modulation by the task.

Group BSS results

Three source components showed frequency bandwidths which significantly differed from baseline to navigation at the strict level of significance2 : Components Two,

Five and Six (Table 1). Component Six was deemed to be an artifactual component, thus will be discussed separately. centered), was localized to inferior frontal regions and showed significantly greater 3 A complete re-analysis of the data with a broader frequency range was carried out in order to provide a confirmatory analysis of gamma findings. In order to facilitate analysis with a sampling rate of 128, data was band-pass filtered in the range 1-64Hz. Artifact reduction and group BSS were applied as described.

****Insert

Very similar sources, both in terms of spatial localization and spectral properties were observed as part of the BSS results. Most pertinent, Component Five showed a similar source profile, with 3 of the strongest 9 voxels localized to parahippocampal gyrus, and 3 in the precuneus. Contrasting spectral power on this source during navigation with baseline showed significantly (p<.05) increased 3-5Hz activity, as well as 44-47 Hz, 52-53 Hz & 55-63 Hz activity. These results confirmed the observation of increased gamma activity on the medial temporal/parietal source.

Scalp EEG analysis

Average spectral power at scalp electrode sites was obtained for both baseline and navigation conditions. Representative power spectra at a number of scalp sites are presented for the navigation condition (Figure 5a). Results of the permutation t-max tests showed that only increased power at 1Hz and 2Hz was observed at electrodes F7, F4, and F6. This is presumably the result of such a strict significance threshold and the lower signal-to-noise ratio of scalp analyses. To further probe scalp sites showing theta power differences during navigation, scalp maps of t-scores obtained from contrasting spectral power during navigation with that of baseline spectral power were obtained. Scalp maps of these t-scores in the lower theta band are shown in Figure 5b. Sites where theta power increased from baseline to navigation were largely confined to parieto-occipital regions.

The increased theta in lateral frontal sites is likely the same EOG artifact isolated in Component Six of the BSS solution. Thus, while theta power was maximal around the frontal midline, theta power in this region did not increase from the resting eyes open condition; rather it was theta power in more posterior regions that increased during navigation.

****Insert Figure 5a & 5b****

Discussion

The present study utilized blind source separation and source localization of high density EEG recordings to further explore brain correlates of spatial navigation. In line with predictions two source components showed significant navigation-related activity: a right parietal source with elevated beta and gamma activity, and a right medial temporalparietal source with increased theta and gamma activity. Theta activity from this source was correlated with navigation performance, where greater theta activity was associated with more efficient navigation. These findings are in agreement with the bulk of the literature exploring navigational processing, further supporting recent models of spatial cognition in highlighting the role of parietal and medial temporal lobe regions [START_REF] Byrne | Remembering the past and imagining the future: A neural model of spatial memory and imagery[END_REF][START_REF] Burgess | Spatial cognition and the brain[END_REF][START_REF] Whitlock | Navigating from hippocampus to parietal cortex[END_REF]. That such results were obtained using scalp-recorded EEG suggests non-invasive techniques such as this may offer legitimate tools for the investigation of complex cognitive processes involving generators beyond those immediately proximal to the surface.

Gamma activity in the right parietal region

The right inferior parietal source with activation in beta and gamma bands likely reflects visuo-spatial processing induced by the task. Right lateralized gamma activity has also been reported in a recent iEEG study using virtual town navigation associated with movement-and search-related processing, particularly in temporal, parietal and occipital electrode placements [START_REF] Jacobs | Right-lateralized brain oscillations in human spatial navigation[END_REF]. Given the role of the parietal lobe in numerous aspects of spatial cognition, including basic visuo-spatial processing and egocentric representations of space, gamma activity in this region likely reflects the importance of these processes during spatial navigation.

Theta/gamma activity in right medial temporal-parietal regions

Given that the BSS method we employed (AJDC) specifically seeks uncorrelated components with non-proportional power-spectra, multiple regions identified on a single component can be conceived as a network oscillating in-phase (with same or opposite sign) at the same frequency. Thus, in the present research a medial temporal-parietal network emerged during spatial navigation displaying in-phase theta activity, as well as in-phase gamma activity. Whilst the medial temporal lobe has long been implicated in allocentric representations of the environment, and the precuneus of the parietal lobe thought to be important for egocentric representations of environment, our findings suggest a coupling between the two regions during navigation. Such coupling will form the object of a dedicated investigation.

Whilst the use of a resting eyes-open contrast condition does not control for nonspecific factors, the observation that this theta activity is correlated with navigation performance links this activity with efficient navigation. Gamma and theta oscillations have been linked with numerous aspects of human spatial navigation using iEEG [START_REF] Caplan | Distinct patterns of brain oscillations underlie to basic parameters of human maze learning[END_REF][START_REF] Ekstrom | Human hippocampal theta activity during virtual navigation[END_REF]Jacobs et al., 2009) and MEG [START_REF] Cornwell | Human hippocampal and parahippocampal theta during goal-directed spatial navigation predicts performance on a virtual Morris water maze[END_REF]2010;[START_REF] De Araújo | Theta oscillations and human navigation: A magnetoencephalography study[END_REF]. Our findings replicate observed theta and gamma oscillations within parietal and medial temporal regions during spatial navigation. We speculate that the theta and gamma oscillations are coherent, providing a mechanism by which medial temporal and parietal brain regions communicate during navigation; an important piece of the puzzle currently missing from the spatial navigation literature [START_REF] Moser | Place cells, grid cells, and the brain's spatial representation system[END_REF]. Interestingly, it has been suggested that findings from spatial navigation research may provide insights into other aspects of cognitive brain function [START_REF] Buzsáki | Theta rhythm of navigation: Link between path integration and landmark navigation, episodic and semantic memory[END_REF], and mounting evidence suggests a functional link between theta and gamma oscillatory activity and cortico-hippocampal communication as part of a broader mechanism of processing within the brain [START_REF] Babiloni | Hippocampal, amygdala, and neocortical synchronization of theta rhythms is related to an immediate recall during Rey Auditory Verbal Learning Test[END_REF][START_REF] Lisman | A neural coding scheme formed by the combined function of gamma and theta oscillations[END_REF][START_REF] Sirota | Entrainment of neocortical neurons and gamma oscillations by the hippocampal theta rhythm[END_REF].

Hippocampal activity, the theta rhythm, and scalp EEG Whilst medial temporal activity reported herein included activity in medial temporal voxels, there exists a view that the hippocampus is a closed field structure and thus does not contribute to scalp recorded EEG. Owing to its cylindrical structure, it has been proposed that neurons of the hippocampus form a radially symmetric field, and there is evidence to suggest some aspects of hippocampal activity are not detected immediately beyond the structure itself [START_REF] Fernández | Real-time tracking of memory formation in the human rhinal cortex and hippocampus[END_REF][START_REF] Pascual-Marqui | Standardized low resolution brain electromagnetic tomography (sLORETA): Technical details[END_REF][START_REF] Rosburg | Hippocampal event-related potentials to tone duration deviance in a passive oddball paradigm in humans[END_REF]. However, early modelling on which this notion was based employed a simplified closed sphere of simultaneously active neurons not designed to simulate the hippocampus [START_REF] Klee | Computed potentials of cortically arranged populations of neurons[END_REF], and more recent modelling of deep brain structures, including the hippocampus, challenge the notion that hippocampal activity cannot be detected using EEG and MEG recordings [START_REF] Attal | Modelling and detecting deep brain activity with MEG and EEG[END_REF]. Given these unresolved issues, combined with the use of a standard head model, we take the conservative view that the medial temporal lobe activity reported herein reflects parahippocampal theta activity. Given the strength of afferent and efferent hippocampal projections with the surrounding parahippocampal regions (indeed, parahippocampal cortex "accounts for the large majority of the cortical input to the hippocampus", Burwell, 2000, p.25), such activity may provide an ideal window on hippocampal function using non-invasive recordings.

Future research incorporating task-related intracranial EEG from hippocampal regions and surface EEG recordings will enable greater insights as to the contribution of hippocampal activity in surface recorded EEG.

Source localization with AJDC and sLORETA

EEG recordings from a given electrode sensor represent the summed activity of multiple source signals, plus extra-cerebral sources (eg. EOG), as a result of volume conduction (see [START_REF] Congedo | On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics[END_REF] for a review). When exploring deeper cortical sources these effects are exacerbated, making source localization attempts from EEG even more difficult (eg. [START_REF] Wagner | Evaluation of sLORETA in the presence of noise and multiple sources[END_REF]. BSS methods such as AJDC may be a suitable method for counteracting volume conduction effects which lead to the 'poor spatial resolution' of EEG recordings. Whilst source localization of ERP data has provided suggestive evidence of memory-related medial temporal lobe activity, averaged responses across many trials are required to enhance signal-to-noise ratio [START_REF] Alhaj | Effects of DHEA administration on episodic memory, cortisol and mood in healthy young men: A double-blind, placebo-controlled study[END_REF]. BSS techniques combined with source localization provide a useful tool for exploring task-related EEG (eg. Delorme et al., 2007b), as well as applications in realtime EEG fields such as neurofeedback and brain-computer interfaces [START_REF] Congedo | Subspace projection filters for real-time brain electromagnetic imaging[END_REF] and clinical studies comparing patients to normative databases [START_REF] Congedo | Group independent component analysis of resting-state EEG in large normative samples[END_REF].

Limitations and conclusions

The use of a resting eyes open 'baseline' condition for comparison is a common method adopted in a variety of fields in cognitive neuroscience, but is not without problems (eg. [START_REF] Stark | When zero is not zero: The problem of ambiguous baseline conditions in fMRI[END_REF]. Pre-task resting conditions have been shown to have their own electrophysiological correlates [START_REF] Chen | EEG default mode network in the human brain: Spectral regional field powers[END_REF][START_REF] González-Hernández | A pre-task resting condition neither 'baseline' nor 'zero[END_REF], suggesting caution is necessary when interpreting analyses with a resting eyes open comparison condition. While a line-following control condition has been used in fMRI and PET navigation studies (eg. [START_REF] Hartley | The well-worn route and the path less traveled: Distinct neural bases of route following and wayfinding in humans[END_REF][START_REF] Iaria | Retrosplenial and hippocampal brain regions in human navigation: Complimentary functional contributions to the formation and use of cognitive maps[END_REF], EEG studies have reported theta and gamma activity associated with a number of behaviours associated with virtual movement [START_REF] Caplan | Human theta oscillations related to sensorimotor integration and spatial learning[END_REF]Jacobs et al., 2009)

  Baseline and navigation recordings were re-referenced to a linked mastoid reference, and band-pass filtered (FIR, 24dB/Octave) in the range 1-44 Hz using Scan 4.3 Edit Software (Neuroscan, Inc.). The first eight seconds of navigation to each of the six landmarks were selected for each participant. Eight seconds corresponded to the minimum time for a participant to travel the distance between the closest two landmarks, ensuring the participant was actively navigating throughout the selected period. This resulted in 48-seconds of navigation data for each participant (six landmarks x eight seconds). Baseline eyes open data of the same length was selected from approximately 20-seconds into the resting eyes open recording. To facilitate further computation all data

  beta and gamma power during navigation. Whilst the significant voxels comprised an extended area, the sLORETA solution shows a focal right parietal distribution, centered in inferior parietal gyrus. Component Five was largely localized to right parietal and medial temporal lobe regions, with significantly elevated theta power during navigation. As with de Araújo et al. (2002), peak theta activity was at the low end of the traditionally defined bandwidth. Unlike Component Two, the sLORETA solution demonstrated multiple sources, with both right parietal and medial temporal regions implicated. Using a less stringent significance threshold (0.2/36; p = 0.0056), the active theta bandwidth was larger and a marginally significant increase in gamma power was also noted on this component during navigation 3 . The source localization and power spectra of the two cortical sources found to show navigation-related activity are presented in Figures 2 and 3. ****Insert Figures 2a, 2b & 3a, 3b**** Component Six demonstrated a bilateral frontal scalp distribution (F7 and F8

  power in the 1-6 Hz bandwidth during navigation. This was deemed eye movement artifact; induced by exploring the visual environment presented within the task. Whilst the most energetic eye artifact component was removed during pre-processing, the removed artifact was likely eye blinks, and Component Six represents remaining lateral eye movement.Finally, bands showing spectral power increases during navigation forComponents Two and Five were correlated with navigation latency. Continuous bins to show significant increases were pooled, resulting in power estimates for four separate bands (Component Two: gamma (42-44Hz) & beta (28Hz); Component Five: theta (3-6Hz) & gamma (44Hz)), which were then log transformed. Spectral data for each band was then plotted against navigation latency, inspected for outliers, and Spearman rank correlations calculated. Two outliers were removed from the correlation analyses. A significant negative association between Component Five theta and navigation latency was observed (r s =-.659, p=.001). Greater theta power on this medial temporal-parietal source was associated with more efficient navigation (see Figure4). No association was observed between spectral data for Component Two gamma, beta, or Component Five gamma, and performance.

  , suggesting a line-following control is inappropriate in such studies.[START_REF] De Araújo | Theta oscillations and human navigation: A magnetoencephalography study[END_REF] explored an appropriate control condition in an MEG study of human navigation, focussing on theta activity. These researchers excluded frontal midline theta, the motor demands of the task, and passive viewing of the visual environment as factors explaining enhanced theta in virtual town navigation, albeit using MEG sensors largely distributed over temporal regions. Finally, after reporting no differences between eyes open resting and stationary route-planning conditions, navigation data was compared to a pooled control condition using both of these stationary conditions. Scalp maps of theta activity associated with navigation in the present study also indicated that increased theta activity originated from posterior scalp sites, and not frontal midline locations. While the present findings require replication and extension, viewed in light of the correlation between the observed theta activity and navigation performance, the use of an eyes open resting condition for comparison appears sufficient.A further limitation of our study is the use of a standard head model and standard electrode coordinates, which lowers the spatial resolution and precision of our analysis. A better procedure would be to use individual head models and actual electrode coordinates to compute the inverse solution in each individual and then co-register the result in a standard head model.Brain oscillatory activity associated with human spatial navigation has been difficult to study due to the depth of implicated brain regions, and the limited information gained from traditional analysis of non-invasive recording techniques. Using AJDC coupled with source localization, two source components were found to show navigationrelated activity: a right parietal source largely with gamma activation, and a right medial temporal-parietal source with theta and gamma activation. These findings further suggest theta activity is involved in integrating information from medial temporal and parietal regions active during spatial processing, while also implicating gamma activity in this role. Developing analysis techniques, such as AJDC coupled with source localization procedures, may enable insights into deeper brain processes from surface EEG recordings.

Figure 1 .

 1 Figure 1. Navigation task. (a) Layout of the town with stars indicating location of landmarks. (b) First-person view from within the town.

Figure 2 .

 2 Figure 2. (A) sLORETA source map of Component 2, from left to right: axial, sagittal, and coronal sections. A = anterior, P = posterior, L = left, R = right. (B) Power spectra for Component 2 during navigation and baseline conditions.

Figure 3 .

 3 Figure 3. (A) sLORETA source maps for Component 5, from left to right: axial, sagittal, and coronal sections. A = posterior, P = posterior, L = left, R = right. (B) Power spectra for Component 5 during navigation and baseline conditions.

Figure 4 .

 4 Figure 4. Scatterplot of navigation latency against Component 5 theta (3-6 Hz) power during navigation.

Figure 5 .

 5 Figure 5. (A) Representative grand average power spectra for scalp electrode sites Fz and CPz during navigation. (B) Scalp maps of t scores for average spectral power from 3 to 6 Hz during navigation contrasted with baseline spectral power. Positive t scores indicate greater power in navigation condition.

Table 1

 1

	****

Available for free download at http://www.lis.inpg.fr/pages_perso/congedo/MC_Software.html

At a less stringent level of significance (p<.05), a number of other sources displayed spectral power differences from baseline to navigation. These included a posterior alpha source (maximal in precuneus) demonstrating significantly greater 9-13 Hz activity during baseline than navigation. Further, a source localised to left precentral regions showed greater relative power from 9-12 Hz during baseline, and significantly elevated 43-44 Hz absolute power during navigation. This likely reflected motor processing associated with right-hand keyboard presses required for navigation.

Acknowledgements

The authors wish to thank the assistance of Julian Brown with task development and image preparation, Shaun Seixas with EEG data collection, and the anonymous reviewers for their valuable comments.


Sakkaravarthi Ramanathan
email: sakkaravarthi@laas.fr

Aymen Kamoun
email: akamoun@laas.fr

Khalil Drira

Christophe Chassot
email: chassot@laas.fr

Ontology-based collaborative framework for disaster recovery scenarios

Keywords: Semantic Modeling, Event-Based Communication, Adaptation, Self-Reconfiguration, Ontology, Disaster recovery applications I

published or not. The documents may come

INTRODUCTION

Disaster is a natural hazard containing forest fire, earthquake, flooding, etc. It can be defined as a serious disruption of the functioning of a community or a society causing widespread human, material, economic or environmental losses which exceed the ability of the affected community or society to cope using its own resources. The total systematic coordination activities for the prevention and respectively the coverage of natural and man-made disasters are termed as disaster management activities [START_REF] Mecella | WORKPAD: an Peer-to-Peer Software Infrastructure for Supporting Collaborative Work of Human Operators in Emergency/Disaster Scenarios[END_REF]. Here, different actors like firemen, robots along with human volunteers operate together to save and rescue people.

At the intervention area, wireless communication among the actors is more appropriate as it is not reliable to depend on the wired network. But the availability of this medium depends heavily on the actor's movements to save the victim. Also, the smart devices greatly vary in terms of e.g. processing and storage capabilities, energy consumption, and networking technologies. As communication resources could be lost or in demand, we need to have adaptive solutions to cope up the challenges posed by this environment. Thus, this work presents a design of ontology based collaborative autonomous system that provides adaptive solutions for achieving the mission tasks. Software agent is deployed at actor's device that triggers the control center if there is a problem. Once alarmed at control center, semantic model (ontology) and rules reconfigure dynamically its topology to ensure minimum guarantees to maintain the mission tasks.

This paper is structured as follows. Section 2 presents related work and section 3 outlines our disaster recovery scenario with brief introductions. Section 4 details our proposed approach. Section 5 focuses on our implementation in an adaptive architecture called FACUS. Section 6 concludes this work.

II. RELATED WORK

Several researches have been carried out for collaborative systems and session management. A majority of these solutions deal with different aspects of collaboration. However, very few works treat specifically the problem of providing tools for building context-aware collaborative applications with dynamic reconfiguration of components at runtime.

We have studied existing synchronous collaborative systems such as TANGO [START_REF] Beca | Tangoa Collaborative Environment for the World-Wide Web[END_REF], HABANERO [START_REF] Chanbert | NCSA Habanero-Synchronous collaborative framework and environment[END_REF], DOE200 [START_REF]EMSL DOE2000: Real-time Collaboration Management Project Summary[END_REF] and DISCIPLE [START_REF] Marsic | Real-Time Collaboration in Heterogeneous Computing Environments[END_REF]. The main lack in these systems is that the all member roles cannot be changed dynamically during the collaborative activities. Consequently, these systems cannot support dynamic reconfigurations to maintain collaboration within structured sessions. Thus, model-based approaches are required in order to ensure the flexibility in the described systems. Other sessions models describe only three components of a session: users, tools and data flows [START_REF] Edwards | Session management for collaborative applications[END_REF][START_REF] Dommel | Group Coordination Support for Synchronous Internet Collaboration[END_REF]. These proposed models establish collaborative sessions by monitoring members' activities inside groups. Some of them support dynamic change and provide a representation of the sessions but this representation is too specific to the model, what restricts its use in other collaborative systems.

Other ontologies-based works are proposed and applied to different problems of CSCW. Andonoff et al. [START_REF] Andonoff | A protocol ontology for inter-organizational workflow coordination[END_REF] proposes ontology of high level protocols for agents' conversations. Ontologies are used in order to provide semantic to these protocols and to ensure automation of coordination in distributed systems. Garrido et al. [START_REF] Garrido | Definition and use of Computation Independent Models in an MDA-based groupware development process[END_REF] propose an MDA-based approach for modeling enterprise organization and developing groupware applications. The domain model is formalized through domain ontology in order to describe relations between actors sharing knowledge. Tomingas and Luts propose a semantic interoperability framework for data management like web services descriptions and ontologies [START_REF] Tomingas | Semantic Interoperability Framework for EstonianPublic Sector's E-Services Integration[END_REF].

The main disadvantage of classical collaborative systems is the lack of rigid deployment services of components or application that they offer. Components and applications are often deployed manually on the different machines and fixed at design time by a static way. This method cannot be applied to situations that need a high degree of adaptation, and in which even not known components should be deployed in advance.

Ontology has received great attention in the recent years, due to their use for knowledge representation in the Semantic Web domain. The Semantic Web was proposed by Tim Berners-Lee [START_REF] Berners-Lee | A Roadmap to the Semantic Web[END_REF] in order to enrich data contained in the World Wide Web. The main idea is to add metadata in order to describe Web data (which is only human readable) in order to make it understandable by machines, thus enabling the automation of distributed processing over the Web. Metadata describing the semantics of contents is expressed in several languages such as RDFS (Resource Description Framework Schema, based on RDF) and OWL (Web Ontology Language). OWL is the Semantic Web standard for representing ontologies [START_REF] Smith | OWL Web Ontology Language Guide[END_REF], which are common vocabularies allowing to model and represent knowledge. The main elements of ontologies are concepts, relations (between two concepts), individuals and axioms. All these elements are based on well-known formalisms such as Description Logics [START_REF] Baader | The Description Logic Handbook. Theory, Implementation, and Applications[END_REF] in the case of OWL. Thus, knowledge can be automatically deduced by inference engines or (for example, Pellet [START_REF] Sirin | Pellet: A practical OWL-DL reasoner[END_REF]). These software elements can process ontology in order to make explicit the implicit knowledge that they use. Also, rules (expressed in SWRL, the Semantic Web Rule Language [START_REF] Horrocks | SWRL: A Semantic Web Rule Language Combining OWL and RuleML[END_REF]) may be included in ontologies and processed by reasoners. Rules add some expressivity to OWL constructs.

We have chosen an ontology-based model because it constitutes a standard knowledge representation system, allowing reasoning and inference. Moreover, ontologies facilitate knowledge reuse and sharing through formal and real world semantics. Therefore, ontologies are high-level representations of business concepts and relations. These representations are close to developers' minds and therefore well suited to depict application level models. We have chosen to describe these models in OWL, the Semantic Web standard for metadata and ontologies.

For these reasons, ontologies seem a good choice for the representation of shareable collaboration knowledge. Standard tools are available and can be used for building and querying ontologies' instances. It also enables the sharing of collaboration concepts between several applications.

Moreover, the use of reasoning and rules such as SWRL is very useful. For example, they allow deducing, at runtime, the collaboration schema that corresponds to a given collaboration configuration required by the application.

Motivated by the above discussion, we present a novel ontology-based support for reconfigurable adaptive group communication architecture at control center. This approach improves the decision making that readily acts in a timeconstraint situation.

III. SCENARIO

The three major roles assigned to this scenario are: mission supervisor, coordinators, and field investigators. Each actor plays his own role and is associated with an identifier and the devices he uses. The performed functions are as follows:

The supervisor's function is to monitor, manage and authorize actions to coordinators and investigators. This entity supervises the whole mission.

Coordinator's job is to report to the supervisor. He manages the group of investigators during the mission and assigns tasks to each one of them. The investigator's role is to explore the operational field, observe, analyze, and report about the situation. They also take care of helping, rescuing and repairing.

Interactions between these actors are achieved by coordination and cooperation flows. Coordination flows take place between investigators and their coordinator and between the coordinators and the supervisor. Cooperation flows occur between the investigators within the same group (A2A type: fireman2fireman, robot2robot, etc.) or between the investigators of different groups (A2B type: robot2fireman, AAV2fireman, etc.). In case of A2A, a distinction is made between the flows such as cooperation notifications, cooperation requests and cooperation suggestions. In the case of A2B cooperation flows, the flows are: cooperation notifications and cooperation requests.

Figure 1 represents a supervisor, two coordinators, 3 firemen and 3 robots. The supervisor and coordinators have WiFi routers that are interconnected and the firemen and robots are connected to their respective coordinators through WiFi infra structure mode.

If fireman 2 wants to communicate to robot1, the path will pass through coordinator, supervisor and coordinator of that robot. As the main goal is to help the victim, there exist four states. The first state is to trace the position of victim or own team members in case of danger. Once traced, the fireman moves to assist the injured person depending on its situation. This state sometimes involves no interference of coordinator but at times, the coordinator send request to fireman to locate the injured. Once the alarm is activated from the recovery team, the neighbour can recognize this situation and appropriate actions are taken. This state diagram helps the designer to understand the interactions between the coordinator and the investigator in an abstract way.

Once the connection is established between the actors, the system should be aware of the mission evolution. The system has to monitor, detect the changing environment and adapt to the evolution. It should handle and manage the connection between the control center as well as the other group members. If there is a failure in doing so, it should be smart enough to repair by activating suitable functions. It is hard to diagnose problems manually in wireless communication system. A good solution is to automate a problem management task by continuously monitoring network condition, analyze the problem when it is detected, and taking adaptation actions for self-recovery. We have to detect, identify, isolate and determine the root cause of problems to recover the system. Problem detection measurements could be local and at the control center. Local measurements are resource usage monitoring such as CPU, memory and battery. It is used to estimate its own health status by investigator itself.

IV. PROPOSED APPROACH

The adaptive techniques at two different states are explained in this section. We will elaborate the techniques used at the control center. At control center, the main feature is the clear partitioning of adaptive functionalities into different levels, in which each level only takes care of the functions that are most suitable to be concerned by it. Each level encapsulates issues into a specific model, thus abstracting complexity to a higher level. The modules at the higher level are abstract system representations that tend to resemble human activities, while lower ones are much closer to real implementations of abstractions supporting these activities. Relevant levels are identified and adaptation at the highest levels should be governed by changes in development of activity requirements. Adaptation at the lowest levels should be driven by execution context constraint changes.

In our work, we aim to support collaboration in distributed environments. New mechanisms are needed for managing session evolution and constraints changes. In our view, semantic web techniques are well suited to achieve this task. As far as we know, there is no existing collaborative system that use semantic for session management and dynamic component deployment. As thus, a semantic driven framework has been developed to enable session management and dynamic components deployment for collaborative systems.

The proposed framework is based on a multi-level modeling architecture (Figure 2). It ensures both high level and low level adaptation: The high level adaption depends on the configuration of different actors in the collaborative system such as their arrival, departure and role changes. While low level adaptation involve low level constraints such as energy level on the devices. SWRL rules are used in order to ensure the adaption at runtime. Some rules allow modeling the exchanged data flow between collaborating entities. Algorithms including different policies of low level constraints adaptation are also used in order to ensure the system robustness and the collaboration continuum.

A. Application Level

This level represents the applications that need collaboration inside the group of devices. It includes software elements implementing the application's business, as well as user interfaces, etc. Among these elements, (at least) those relevant to collaboration are represented in the architectural model corresponding to this level of abstraction. Only collaboration-related elements of the application level model will be taken into account in the refinement process. Nevertheless, other business elements (non collaboration-related) are also included and can be used in order to represent the whole activity.

B. Collaboration Level

This central level describes the way members in a group are organized within sessions, where they can send and receive data flows. The main issue is that of determining a high-level collaboration schema that meets the needs of application's collaboration. Hence, it supports collaborative sessions and can determine those elements needed to implement these sessions. In [START_REF] Garrido | Definition and use of Computation Independent Models in an MDA-based groupware development process[END_REF], an ontology model, containing generic collaboration knowledge as well as domain-specific knowledge, is proposed in order to enable architecture adaptation and to support spontaneous and implicit sessions inside groups of humans and devices.

The collaboration level model is a graph, inspired by dynamic collaboration ontology that models one or more session. A session is a set of data flows. Each data flow goes from a sender component to a receiver component (components are deployed on devices). Sender and receiver components may have text, audio or video as types. Flows are labelled with data types (audio, text and video) and the session to which they belong.

C. Middleware Level

This level provides a middleware model that masks lowlevel details (like TCP sockets, UDP datagrams, IP addresses, multicast, etc.) in order to simplify the representation of communication channels. In actual fact, this level furnishes an abstract view of distributed systems, so that they become transparent for upper levels. For example, this model may be based on abstractions like Event-based Communications, Peer-to-Peer, Remote Procedure Calls or Remote Method Invocation.

Here, we have retained the Event-Based Communication (EBC) [START_REF] Meier | Taxonomy of distributed event-based programming systems[END_REF]. It represents a well established paradigm for interconnecting loosely coupled components and it provides one-to-many or many-to-many communication pattern. This model is a detailed graph containing a set of event producers (EP), event consumers (EC) and channel managers (CM) connected with push and pull links. Multiple producers and consumers may be associated through the same CM. Since this model represents a graph, it can also be expressed in the GraphML language.

D. Application-Collaboration Refinement

Reasoning based on SWRL rules is used in order to implement the application-collaboration refinement. SWRL rules are applied to an instance of the domain ontology that extends the GCM. The proposed Generic Collaboration Meta-model includes a set of generic rules that express some relations and especially those which allow to infer a collaboration schema from the domain ontology instance. However, these rules are not sufficient for complete implementation of the refinement from the domain ontology to the collaboration ontology. Therefore the application designer has to specify additional rules in the domain application model which contains a part of the refinement process. The processing of the SWRL rules produces an instance of the Generic Collaboration ontology represented by a collaboration graph expressed in the OWL language. The application-collaboration refinement produces a single collaboration model from a given application level model.

E. Collaborationmiddleware Refinement

As the application level and the middleware level models are represented by graphs, graph grammar theories represent an appropriate formalism to handle the refinement process.

We provide a graph grammar-based implementation of the refinement. We use a graph grammar, that addresses the refinement of a given activity level architecture to all possible EBC level architectures using Graph Matching Transformation Engine, GMTE [START_REF] Rodriguez | A rule-driven approach for architectural self adaptation in collaborative activities using graph grammars[END_REF]. The productions of this graph grammar consider data collaboration components (e.g. \texttt{ReceiverComponent and SenderComponent) as non-terminal nodes and EBC entities (EPs, ECs and CMs) as terminal nodes. A session involving several senders and receivers is refined as a CM connected to several EPs and ECs.

F. Middleware adaptation and selection

Given a set of possible middleware descriptor, it is necessary to select the best adapted architecture to be effectively deployed. We present here a procedure, Select() (Figure 3), that allows selecting one architecture depending on several parameters. This procedure uses the resources context (e.g. energy level, bandwith, etc.) to discard the set of architecture that cannot be deployed within the current resources levels. Among the set of selected architectures, the best configuration is selected by processing a set of defined policies. The choice of a middleware descriptor must take into account the resources context at first. For that, The context_Adaptation() function (Figure 3, line 5) which is a generic function that manages the resources context is used. It can express the availability level of a given resource (bandwidth, memory, energy level, etc.). This function is used for two purposes: first, it allows discarding not adapted architectures that cannot be deployed within the current resources context. Second, it allows selecting the best adapted architectures to that context. This function assigns a value to a given architecture depending on its degree of adaptation to the current context. If the architecture is not compatible with the current context, its value will be -1. Otherwise, it will receive a positive value. Best suited architectures will be assigned higher values.

When several architectures have been assigned the same value of Context_Adaptation(), a policy (indicated by the parameter Policy) is used by the Select() procedure in order to select the optimal configuration. If the chosen policy is Dispersion, the selection is based on maximizing the function Dispersion() (Figure 3, line 8). This generic function is defined as the number of software components deployed per node. If the chosen policy is Distance, the selection minimizes the distance between two middleware descriptors. This is performed using the function Relative_Cost() (Figure 3, line 13).

G. Reconfiguration Rules

To make this system adaptable, reconfiguration rules are needed to adapt the ontology instance to the current situation. As events play a major role in our application, the transformation of entities needs to be triggered. Here, events could occur at activity level, i.e, addition of new participants, changing an action, transfer of a participant from one group to another, new connection between investigators from different groups, etc. The events could also take the form of resource context changes, e.g, parameters like the energy of a device, bandwidth range, CPU processing capacity, RAM availability etc. We use SWRL rules to define our adaptation policy. The application designer defines these rules according to context changes he wants to handle. If there is no solution for an event at a particular level, then it triggers the higher level. In SWRL, the head points to the adaptation transformations whereas the body indicates the context of ontology elements. This reconfiguration rules are really useful for adapting the scenario dynamically.

V. IMPLEMENTATION We implemented our work using the proposed Framework that supports semantic adaptation enabling the awareness of the presence/absence, roles and tasks of collaborators. This framework is based on a generic multilevel modeling approach that ensures multi-level adaptation. A generic collaboration model, based on Semantic Web technologies is proposed in order to support real-time collaboration between groups of participants working together in different tasks. The framework defines common interfaces for collaborative systems to enable the management of cooperative actions.

In the implementation, we have chosen one group composed of a supervisor, two coordinators and two firemen investigators to show the adaptation. Initially, the supervisor and the two coordinators are connected using WiFi infrastructure. Also, the connection between the firemen coordinator and the two investigators (fireman1 and fireman2) are connected in WiFi infrastructure mode.

A. Experiments

The Table1 represent the variation of the energy level of each device during the two phases: phase1 and phase2. We notice that the energy level of the investigator 1' device decreases to 50 during the phase 2. Investigator(?inv) ∧ sessions:Node(?ninv) ∧ sessions:Device(?dinv) ∧ sessions:hasRole(?ninv, ?inv) ∧ sessions:hasHostingDevice(?ninv, ?dinv) ∧ FiremanCoordinator(?coo) ∧ sessions:Node(?ncoo) ∧ sessions:hasRole(?ncoo, ?coo) ∧ sessions:Device(?dcoo) ∧ sessions:hasHostingDevice(?ncoo, ?dcoo) ∧ sessions:hasSameSSID(?dinv, ?dcoo) ∧ hasSignalWith(?dinv, ?dcoo) ∧ sessions:belongsToSameGroup(?inv,?coo) ∧ differentFrom(?inv, ?coo) ∧ sessions:belongsToGroup(?coo, ?t) ∧ hasFiremanCordInvSession(?t, ?s) ∧ swrlx:createOWLThing(?af1, ?inv) ∧ swrlx:createOWLThing(?af2, ?inv) → sessions:AudioFlow(?af1) ∧ sessions:hasSource(?af1, ?ncoo) ∧ sessions:hasDestination(?af1, ?ninv) ∧ sessions:belongsToSession(?af1, ?s) ∧ sessions:AudioFlow(?af2) ∧ sessions:hasSource(?af2, ?ninv) ∧ sessions:hasDestination(?af2, ?ncoo) ∧ sessions:belongsToSession(?af2, ?s) Figure 5: an example of SWRL rule

Role

The figure 6 represents the high level collaboration graph that models the actor needs. Given the configuration of the application level (figure 4), a set of SWRL rules allows inferring the collaboration schema that describe the necessary components (receiver and sender) for each node. The figure 5 describes the SWRL rule allowing establishing the communication between the fireman coordinator and investigators by creating senders and receivers. This rule may be described as follows: if the application contains an investigator and a fireman coordinator, and they have the same SSID, and they can bee connected (hasSignalWith), and tey belong to the same group, so two flows can be created: the first one sent by the investigator and received by the fireman coordinator and the second one will be sent by the fireman coordinator and received by the investigator. Other SWRL rules are created in order to ensure the connection between the robot coordinator and the associated investigators and between the supervisor and the coordinators.

After having processing the SWRL rules, we obtain the collaboration graph represented by the figure 6. This graph represents exchanged data flows between senders and receivers. Each vertex is composed of 5 attributes: the vertex' id, the vertex' type (sender or receiver), the ip address, the exchanged data flow and the name of the session in which the node collaborate. For the created session between the supervisor (10.193.255.1) and the two coordinators (10.193.255.100 and 10.193.255.200), 2 receiver components allow the supervisor to receive data flow from the 2 coordinators and a sender component to send data flows to the two coordiantors. The same type of components will be provided for the created session between the fireman coordinator (10.193.255.100) and the two investigators (10.193.255.143 and 10.193.255.146).

Given the collaboration descriptor, the GMTE will generate all possible EBC-based middleware graphs deploying the ECs, EPs and the CMs on different devices. It will replace each receiver by an EventConsumer (EC) and each sender by an EventProducer (EP). A broker called ChannelManager (CM) will be created for each session. After that, the selection procedure selects the best adapted middleware graph depending on several parameters such as the energy levels. The figure 7 represents the selected middleware graph for the pahse1. Each vertex in this graph is composed of 6 attributes: the vertex' id, the component type (EP, EC or CM), the exchanged data flow, the session name and the ip address of the device on which the component will be deployed. As represented by this figure, EPs have to be deployed for each sender; ECs have to be deployed for each receiver and CMs have to be deployed for each session. For the session including the supervisor and the two coordinators (sup_coor_session), the CM will be deployed on the fireman coordinator' machine (10.193.255.100) because the energy levels are taken into account by the selection procedure (the fireman coordinator has a higher energy level (90) than the supervisor (86) and the robot coordinator (79) (table1)). For the session including the fireman coordinator and the two investigators (Firecoor_inv_session), the CM will be deployed on the Fireman2' machine (10.193.255.143) because the energy levels are taken into account by the selection procedure (the Fireman1 has a higher energy level (93) than the fireman coordinator (90) and the fireman2 (88) (table1)).

Phase2 (adaptation)

In phase 2, the energy level of the fireman 1 decreases from 93 to 50. The selection procedure has to select a new middleware graph more adapted to the current resources context. The figure 8 shows the new selected middleware graph. In this graph, the CM which was deployed on the fireman1'device (10.193.255.143) will be moved to the fireman2'device (10.193.255.146).

Phase3 (arrival of new robot)

The figure 9 represents the middleware graph that contains all required communication component after the arrival of the new robot (10.193.255.202). This investigator has the same SSID as the robot coordinator, so a new session is created enabling the communication between them. This middleware descriptor is obtained after running the adaptation process which generates the application descriptor, collaboration descriptor and the middleware descriptor after the processing of the selection procedure. Here, we show only the middleware graph for simplicity.

VI. CONCLUSION

In this work, a multi-level modeling approach designed to support collaborative adaptation for public safety and disaster applications has been detailed. Here, the whole adaptive process has been divided into different levels.

Throughout the higher levels, ontology has been used where as event-based communication has been retained at the middleware. If a change arises in the environment, reconfiguration can be achieved by using SWRL rules at run-time to handle the mission evolving conditions.

Adaptation is achieved by auto-configuring the system and its associated components after detecting a change in the mission such as new arrival of a actor and problem caused due to resource constraint in communication device.

Figure 1 :

 1 Figure 1: Public recovery application scenario

Figure 2 :

 2 Figure 2: Multi-level architecture

Figure 3 :

 3 Figure 3: Selection procedure

Phase 1

 1 This phase represents the initial state of the collaborating actors. The figure 4 represents the initial configuration: The team Team1 has 5 members: a supervisor having the IP address: 10.193.255.1, a fireman coordinator having the ip address 10.193.255.100, a robot coordiantorhaving the ip address 10.193.255.200 and two investigators having ip address: 10.193.255.143 and 10.193.255.146.

Figure 4 :

 4 Figure 4: The application state

Figure 6 :

 6 Figure 6: the collaboration graph

Figure 7 :

 7 Figure 7: The selected middleware graph

Figure 8 :

 8 Figure 8: The middleware graph after adaptation

Figure 9 :

 9 Figure 9: The middleware graph after arrival of new robot

Table 1 :

 1 Devices' energy levels

		Device (IP)	Energy	Energy
			level	level
			phase1	phase2
	Supervisor	10.193.255.1	86	86
	Fireman	10.193.255.100	90	90
	coordinator
	Robot	10.193.255.200	79	79
	coordinator
	Fireman 1	10.193.255.143	93	50
	Fireman 2	10.193.255.146	88	88

ACKNOWLEDGEMENT

This research is supported by the French project ROSACE (RObots et Systémes Auto-adaptatifs Communiquants Embarquès).