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.
Highlights
The human brain can be considered as a dynamic network changing its configuration at each time instant
Based on this idea of unique synchronisation patterns or synchrostates, this paper proposes the use of the brain networks parameters obtained from the use of the maximum and minimum occurring states calculated during a motor imagery (MI) task
Bashashati et al [22] performed a comparative study using 14 different brain–computer interface (BCI) configurations finding that the logistic regression algorithm and multi-layer perceptron classifiers were among the best in all different designs
Summary
The human brain can be considered as a dynamic network changing its configuration at each time instant. Afterwards, the concept of synchrostates was translated into brain network measures [2] with the aim of identifying the main differences between two groups; one presenting autism spectrum disorder and a healthy participants group used as a control. Based on this idea of unique synchronisation patterns or synchrostates, this paper proposes the use of the brain networks parameters obtained from the use of the maximum (most frequently) and minimum occurring states calculated during a motor imagery (MI) task. EEG recording from ten participants was obtained during the execution of different MI tasks using schematic faces, popularly known as emoticons, showing different emotions as stimuli
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