1. A novel single-trial analysis scheme for characterizing the presaccadic brain activity based on a SON representation K. Bozas, S.I. Dimitriadis, N.A. Laskaris, A. Tzelepi AIIA-Lab, Informatics dept., Aristotle University of Thessaloniki ICCS, National Technical University of Athens

2. Outline Introduction -Every cognitive task is executed in a slightly different way each time, introducing single trial (ST) variability. Methodology -ST variability is self-organized in patterns. -Brain is a complex system, it’s self-organization can be studied via EEG. -Network analysis examines relations between nodes in a graph. Results Conclusions

3. Saccade is a fast movement of eyes. Electrooculogram (EOG) is the standard way to record eye movements. The brain regions involved in saccadic control have not,yet, been completely identified. Here, we deal with normal saccades and the related pre-saccadic brain activity as recorded via EEG IntroMethodResultsConclusions

4. Motivation and problem statement - Can we exploit the single trial variability (ST) observed in saccades? Traditional approaches, like characterization via ERD/ERS, do not take into account ST variability. Manifold learning techniques do. Combined with network analysis, they can provide a framework to analyze brain’s functional connectivity. As in many others cognitive tasks the execution of saccades is characterized by considerable single trial (ST) variability. IntroMethodResultsConclusions

5. Outline of our methodology Single-trial variability is utilized to introduce an implicit experimental control. An approach to provide a detailed characterization of presaccadic brain activity. Network analysis is performed for each group individually and the inter-group comparison reveals the essence of saccadic control mechanism Based on EEG activity and the network of electrodes the notion of functional connectivity graph (FCG) topology, is utilized to identify different modes of brain’s self organization. Saccades are organized in groups of different velocity patterns. The associated brain activity is organized accordingly IntroMethodResultsConclusions

6. Data acquisition: Go-No Go experiment duration = 1000ms duration = 2000ms (2500  500) duration =2500  500ms duration=2000ms t 9 subjects 64 EEG electrodes Horizontal and Vertical EOG Trial duration: 8 seconds 7-9 runs, 40 trials per run 70-90 trials for each condition 4 Conditions: Go Right No-Go Right Go Left No-Go Left IntroMethodResultsConclusions

7. A single trial 2000 ms 2500±500 ms Trigger #1 Trigger #{2,3,4,5} Trigger #{6,7} onset 1000 ms Relax period 2500±500 ms End of trial 8000ms or t Latencies of interest IntroMethodResultsConclusions

8. Saccadic onset detection in EOG signals 1. Calculate EOG velocity. EOG (y) EOG velocity (dy/dt) 2. We look back and forth in time using a linked double window. we have detected a saccadic onset. 3. According to D.E. Marple-Horvat et al. (1996) paper. IntroMethodResultsConclusions

9. Introducing experimental control Each saccade differs in execution speed. We attempt to organize the EOG velocity variations in prototypical patterns. A self-organized artificial neural network, Neural-Gas, is employed to learn the ST-variability. We end up with three control groups, corresponding to SLOW, FAST and VERY FAST saccades. IntroMethodResultsConclusions

10. Neural-Gas algorithm Neural-Gas algorithm provides input space representations by constructing data summaries ( via prototypical vectors ). Its a gradient descent procedure imitating gas dynamics within data space to calculate the prototypes. Using the Voronoi-diagram of prototypical vectors, we classify each saccade to the closest prototype. IntroMethodResultsConclusions

11. Applying Neural-Gas ST Segment to be fed in Neural-Gas (100ms) Neural-Gas for 3 prototypes Append each saccade to the closest prototype and group the corresponding EEG trials accordingly. IntroMethodResultsConclusions

12. Τhe functional connectivity graph (FCG) describes coordinated brain activity - How do we identify the important variations in brain activity underlying the different velocity groups? Considering the brain as a network, where neuronal groups (nodes) exchange information, we can model brain’s self-organization during saccade execution, by measuring information exchange efficiency among nodes. In order to setup the FCG, we have to establish connections between the nodes (i.e. the 64 EEG electrodes). Phase synchronization, is a mode of neural synchronization, that can be easily quantified through EEG signals. IntroMethodResultsConclusions

13. Phase-locking Value (PLV) PLV quantifies the frequency-specific synchronization between two neuroelectric signals ( Lachaux et. al. 1999 ). We obtain the phase of each signal using the Hilbert transform.  (t, n) is the phase difference φ 1 (t, n) - φ 2 (t, n) between the signals. PLV measures the inter-trial variability of this phase difference at t. If the phase difference varies little across the trials, PLV is close to 1; otherwise is close to 0 IntroMethodResultsConclusions

14. PLV procedure for a pair of electrodes IntroMethodResultsConclusions Adopted from Lachaux et. al. 1999

15. 0.9 0.6 Building the FCG The process is repeated for every electrode, creating a complete graph. Establishing links for a single electrode IntroMethodResultsConclusions

16. Information exchange efficiency over the FCG The network metric of local efficiency ( Latora et. al. 2001 ) is employed to identity brain regions with high activity, and to model brain’s self-organization prior to a saccade. k i corresponds to the total number of neighbors of the current node M is the set of all nodes in the FCG d keeps the shortest absolute path length between every possible pair in the neighborhood of the current node IntroMethodResultsConclusions

17. Method outline The topography of the 64 individual efficiency values is potrayed for different time-intervals before the saccade onset IntroMethodResultsConclusions

18. Go vs. No-Go (S2) Beta band (13-30Hz) High information exchange rate Low information exchange rate Go No-Go IntroMethodResultsConclusions

19. Go vs. No-Go (S6) Beta band (13-30Hz) High information exchange rate Low information exchange rate Go No-Go IntroMethodResultsConclusions

20. Differences between velocity groups Beta band (13-30Hz) Early peaks and high efficiency in fast saccades. IntroMethodResultsConclusions

21. We have introduced a ST-analysis framework for modelling brain’s self-organization during saccadic execution. Our approach can be used to characterize EEG recorded brain activity, originating from any cognitive task. Difficulties in the control of a task during an experiment, can be overcomed using ST self-organization. Our methodology offers novel knowledge about the coding of kinematic parameters related to eye movements. In the future, it can be used to study the neural activity related to the kinematics of arm movements in order to drive neural prostheses. IntroMethodResultsConclusions