Open AccessClassification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm
Author(s) -
Mandal S.,
Singh B.K.,
Thakur K.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2020.2710
Subject(s) - support vector machine , brain–computer interface , electroencephalography , computer science , artificial intelligence , binary classification , pattern recognition (psychology) , working memory , functional near infrared spectroscopy , interface (matter) , machine learning , speech recognition , cognition , psychology , prefrontal cortex , neuroscience , bubble , maximum bubble pressure method , parallel computing
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.