Phase synchrony in subject-specific reactive band of EEG for classification of motor imagery tasks

Abstract

Recent works on brain functional analysis have highlighted the importance of distributed functional networks and synchronized activity between networks in mediating cognitive functions. The network perspective is fundamental to relate mechanisms of brain functions and the basis for classifying brain states. This work analyzes the network mechanisms related to motor imagery tasks based on synchronization measure (PLV (phase-locking value)) in EEG alpha-band for the BCI Competition IV Data Set. Based on network dissimilarities between motor imagery and rest tasks, important nodes and important channel pairs corresponding to tasks for all subjects are identified. The identified important channel pairs corresponding to tasks demonstrate significant PLV variation in line with the experiment protocol. With the selection of subject-specific reactive band, these channel pairs provide even more higher variation corresponding to tasks. This paper demonstrates the potential of these identified channel pairs in task classification for future BCI applications.

Higher Order Statistics and Phase Synchronization as Features in a Motor Imagery Paradigm

“…When the oscillatory phases from two brain regions are correlated the phase synchronization appears. The phase synchronization from the motor imagery period is different from the phase synchronization in the relaxation period so it can be exploited by BCI applications [17].…”

Effect of a Brain–Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity

“…However, studies that used EEG-based BCIs for lower limb rehabilitation by performing MI tasks did not report, to the best of our knowledge, how brain regions connect amongst themselves. The cortical connectivity analysis describes the interactions between brain locations through patterns that represent the dynamics of information flow [ 34 , 36 , 37 ]. Thus, this information may contribute to developing more effective therapy with BCIs by obtaining a classification model based on connectivity from various subjects.…”

Interacting brains coming in sync through their minds: An inter-brain neurofeedback study

“…For example, single-channel alpha enhancement with theta crossover can be achieved in less than 30-minute sessions (Demos, 2005). Recently, it has been shown that not only spectral power in different frequency bands, but also coherence and phase synchronization measures between different electrodes, can be used as NFB features (Brunner et al, 2006;Gonuguntla et al, 2013;Gysels and Celka, 2004;Sacchet et al, 2012;Wei et al, 2007). In coherence training, participants are reinforced when correlation or coupling between signals at different brain sites is altered in a desired way.…”