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Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine

Electroencephalogram-based cognitive load level classification using wavelet decomposition and... Cognitive load level identification is an interesting challenge in the field of brain-computer-interface. The sole objective of this work is to classify different cognitive load levels from multichannel electroencephalogram (EEG) which is computationally though-provoking task. This proposed work utilized discrete wavelet transform (DWT) to decompose the EEG signal for extracting the non-stationary features of task-wise EEG signals. Furthermore, a support vector machine (SVM) implemented to classify the task from the DWT-based extracted features. . The proposed methodology has been implemented on a renowned EEG dataset that captured three levels of cognitive load from the n-back test. In this work, two different approaches: i) Low vs High cognitive load (0-back vs [2-back+3-back]) and ii) Low vs Medium vs High (0-back vs 2-back vs 3-back) are investigated for the performance measurement. The linear SVM achieved the highest average classification accuracy that is 77.20 ± 6.63 and 87.89 ± 7.3 for 3-class and 2-class approaches, respectively. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine

Electroencephalogram-based cognitive load level classification using wavelet decomposition and support vector machine

Abstract

Cognitive load level identification is an interesting challenge in the field of brain-computer-interface. The sole objective of this work is to classify different cognitive load levels from multichannel electroencephalogram (EEG) which is computationally though-provoking task. This proposed work utilized discrete wavelet transform (DWT) to decompose the EEG signal for extracting the non-stationary features of task-wise EEG signals. Furthermore, a support vector machine (SVM) implemented to...
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/lp/taylor-francis/electroencephalogram-based-cognitive-load-level-classification-using-1hmK2kGvHp
Publisher
Taylor & Francis
Copyright
© 2022 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2022.2109855
Publisher site
See Article on Publisher Site

Abstract

Cognitive load level identification is an interesting challenge in the field of brain-computer-interface. The sole objective of this work is to classify different cognitive load levels from multichannel electroencephalogram (EEG) which is computationally though-provoking task. This proposed work utilized discrete wavelet transform (DWT) to decompose the EEG signal for extracting the non-stationary features of task-wise EEG signals. Furthermore, a support vector machine (SVM) implemented to classify the task from the DWT-based extracted features. . The proposed methodology has been implemented on a renowned EEG dataset that captured three levels of cognitive load from the n-back test. In this work, two different approaches: i) Low vs High cognitive load (0-back vs [2-back+3-back]) and ii) Low vs Medium vs High (0-back vs 2-back vs 3-back) are investigated for the performance measurement. The linear SVM achieved the highest average classification accuracy that is 77.20 ± 6.63 and 87.89 ± 7.3 for 3-class and 2-class approaches, respectively.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Aug 8, 2022

Keywords: Electroencephalogram (EEG); cognitive load; classification; n -back test; discrete wavelet transform (DWT); support vector machine (SVM)

References