Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

Abstract

Single modality brain-computer interface (BCI) systems often mislabel the electroencephalography (EEG) signs as a command, even though the participant is not executing some task. In this Letter, the classification of different working memory load levels is presented using a hybrid BCI system. N-back cognitive tasks such as 0-back, 2-back, and 3-back are used to create working memory load on participants while recording EEG and functional near-infrared spectroscopy (fNIRS) signals simultaneously. A combination of statistically significant features obtained from EEG and fNIRS corresponding to frontal region channels are used to classify different N-back commands. Kernel-based support vector machine (SVM) classifiers are employed with and without cross-validation schemes. Classification accuracy of 100% is achieved for binary classification of 0-back against 2-back and 0-back against 3-back using linear SVM, quadratic SVM, and cubic SVM under holdout data division protocol.