Enhanced inter-subject brain computer interface with associative sensorimotor oscillations

Healthcare Technology Letters
Simanto SahaLeontios Hadjileontiadis

Abstract

Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved usi...Continue Reading

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Citations

Feb 13, 2018·IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society·Simanto SahaAhsan Khandoker
Aug 10, 2019·Frontiers in Neuroinformatics·Simanto SahaMathias Baumert
Feb 11, 2020·Frontiers in Computational Neuroscience·Simanto Saha, Mathias Baumert
Nov 17, 2020·Frontiers in Human Neuroscience·Selina C WriessneggerAndreea I Sburlea
Mar 16, 2021·Frontiers in Systems Neuroscience·Simanto SahaMathias Baumert

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