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

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...

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Veröffentlicht in:	Electronics letters Jg. 56; H. 25; S. 1386 - 1389
Hauptverfasser:	Mandal, S, Singh, B.K, Thakur, K
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 10.12.2020
Schlagworte:	binary classification brain‐computer interfaces classification accuracy cognition cross‐validation schemes cubic SVM electroencephalography fNIRS frontal region channels functional near‐infrared spectroscopy signals holdout data division protocol hybrid BCI system hybrid EEG infrared spectra kernel‐based support vector machine classifiers learning (artificial intelligence) linear SVM machine learning paradigm medical signal processing neurophysiology N‐back cognitive tasks N‐back commands quadratic SVM signal classification single modality brain‐computer interface Special Issue: Current Trends in Cognitive Science and Brain Computing Research and Applications statistical analysis statistically significant features support vector machines working memory load classification working memory load levels brain-computer interfaces cubic SVM classification accuracy linear SVM working memory load classification single modality brain-computer interface statistically significant features cross-validation schemes learning (artificial intelligence) binary classification N-back cognitive tasks electroencephalography support vector machines fNIRS cognition hybrid EEG kernel-based support vector machine classifiers hybrid BCI system quadratic SVM working memory load levels machine learning paradigm signal classification medical signal processing frontal region channels functional near-infrared spectroscopy signals N-back commands infrared spectra holdout data division protocol neurophysiology statistical analysis
ISSN:	0013-5194, 1350-911X, 1350-911X
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Beschreibung
Zusammenfassung:	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.
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2020.2710