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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Bibliographic Details
Published in:Electronics letters Vol. 56; no. 25; pp. 1386 - 1389
Main Authors: Mandal, S, Singh, B.K, Thakur, K
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 10.12.2020
Subjects:
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
Online Access:Get full text
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Description
Summary: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