The goal of this study is to determine the intentional movement direction based on the neural signals recorded from the motor cortex using optical brain imaging techniques. Towards this goal, we developed a cross-correlation analysis technique to determine the movement direction from the hemodynamic signals recorded from the motor cortex. Healthy human subjects were asked to perform a two-dimensional hand movement in two orthogonal directions while the hemodynamic signals were recorded from the motor cortex simultaneously with the movements. The movement directions were correlated with the hemodynamic signals to establish the cross-correlation patterns of firings among these neurons. Based on the specific cross-correlation patterns with respect to the different movement directions, we can distinguish the different intentional movement directions between front-back and right-left movements. This is based on the hypothesis that different movement directions can be determined by different cooperative firings among various groups of neurons. By identifying the different correlation patterns of brain activities with each group of neurons for each movement, we can decode the specific movement direction based on the hemodynamic signals. By developing such a computational method to decode movement direction, it can be used to control the direction of a wheelchair for paralyzed patients based on the changes in hemodynamic signals recorded using non-invasive optical imaging techniques.

Σκοπός της παρούσας διπλωματικής εργασίας είναι η μελέτη του αντίστροφου καθορισμού πηγής (inverse source localization problem) και του ρυθμού μ (mu). Έχοντας ως δεδομένο το σήμα του ΗΕΓ γίνεται προσπάθεια µέσω της εφαρμογής της μεθόδου Ανάλυσης Ανεξάρτητων συνιστωσών (ICA) να προσδιοριστούν οι συνιστώσες οι οποίες σχετίζονται με τις περιοχές του εγκεφάλου που ενεργοποιούνται από την κίνηση των χεριών. Με βάση τη λειτουργία της αισθητηριοκινητικής περιοχής του εγκεφάλου και τις ιδιότητες του ρυθμού μ, γίνεται μια μελέτη πάνω στις συνιστώσες που προκύπτουν από την ICA τόσο σε δεδομένα από πραγματική κίνηση, όσο και σε δεδομένα από νοερή κίνηση, καθώς και στην εφαρμογή που μπορεί να υπάρχει σε συστήματα Διεπαφής Εγκεφάλου – Υπολογιστή. / The subject of this diploma thesis is the study of the inverse source localization problem and the mu rhythm. Performing Independent Component Analysis (ICA) on EEG data, we try to specify the components that are related to the brain areas activated by hand movement. By focusing on the function of the somatosensory brain area and the properties or mu rhythm, we study the components resulting from Independent Component Analysis on data of both real and imaginary movement, as well as the possible implementations on Brain – Computer Interface systems.