Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG‐based BCI systems

Abstract

Phase synchronisation between different neural groups is considered an important source of information to understand the underlying mechanisms of brain cognition. This Letter investigated phase-synchronisation patterns from electroencephalogram (EEG) signals recorded from ten healthy participants performing motor imagery (MI) tasks using schematic emotional faces as stimuli. These phase-synchronised states, named synchrostates, are specific for each cognitive task performed by the user. The maximum and minimum number of occurrence states were selected for each subject and task to extract the connectivity network measures based on graph theory to feed a set of classification algorithms. Two MI tasks were successfully classified with the highest accuracy of 85% with corresponding sensitivity and specificity of 85%. In this work, not only the performance of different supervised learning techniques was studied, as well as the optimal subset of features to obtain the best discrimination rates. The robustness of this classification method for MI tasks indicates the possibility of expanding its use for online classification of the brain–computer interface (BCI) systems.

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“…For example, Vankatesan et al 146 designed an SVM classification‐based abnormality detection method wherein extracted R peaks are considered after performing ECG signal preprocessing.The determined classification accuracy of 96% has been attained using existing methods, but the trial result of SVM‐based classifier gives an accuracy of 96% on classifying normal and arrhythmic risk irregular themes. Similarly, Santamaria et al 147 also used SVM classifier along with the projected method combining phase‐synchronization information with clustering, which led to the existence of quasi‐stable states in the order of milliseconds named synchro states during the implementation of different tasks. The alteration of these states, specific for each task, into connectivity networks using graph theory led to the set of features which SVM classified with an accuracy over 80% for the two typical frequency bands.…”

Identifying motor imagery activities in brain computer interfaces based on the intelligent selection of most informative timeframe

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EEG based cognitive task classification using multifractal detrended fluctuation analysis

“…Using inter-band energy ratio of EEG signals, sleep apnea events were also detected and classified (Saha et al 2019). A phase-synchronized cognitive states is analyzed and connectivity metrics is evaluated from motor imagery tasks of perceiving various emotional images (Santamaria and James 2018). Using a single electrode EEG, different entropy features and ensemble classifiers, driver fatigue is evaluated for 12 subjects (Wang et al 2018).…”