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Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy

Mental workload classification with concurrent electroencephalography and functional... AbstractA brain-computer interface that measures the mental workload level of operators has applications in human-computer interactions (HCI) for reducing human error and improving work efficiency. In this study, concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were combined at the decision fusion stage for the classification of three mental workload levels induced by an n-back working-memory task. An average three-class classification accuracy of 42, 43, and 49% has been achieved across 13 participants for the fNIR-alone, EEG-alone, and EEG-fNIRS combined approach, respectively. The current study demonstrated a multimodality-based approach to decode human mental workload levels that may potentially be used for adaptive HCI applications. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Brain-Computer Interfaces Taylor & Francis

Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy

Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy

Abstract

AbstractA brain-computer interface that measures the mental workload level of operators has applications in human-computer interactions (HCI) for reducing human error and improving work efficiency. In this study, concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were combined at the decision fusion stage for the classification of three mental workload levels induced by an n-back working-memory task. An average three-class classification...
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Publisher
Taylor & Francis
Copyright
© 2017 Informa UK Limited, trading as Taylor & Francis Group
ISSN
2326-2621
eISSN
2326-263x
DOI
10.1080/2326263X.2017.1304020
Publisher site
See Article on Publisher Site

Abstract

AbstractA brain-computer interface that measures the mental workload level of operators has applications in human-computer interactions (HCI) for reducing human error and improving work efficiency. In this study, concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) were combined at the decision fusion stage for the classification of three mental workload levels induced by an n-back working-memory task. An average three-class classification accuracy of 42, 43, and 49% has been achieved across 13 participants for the fNIR-alone, EEG-alone, and EEG-fNIRS combined approach, respectively. The current study demonstrated a multimodality-based approach to decode human mental workload levels that may potentially be used for adaptive HCI applications.

Journal

Brain-Computer InterfacesTaylor & Francis

Published: Jul 3, 2017

Keywords: FNIRS; EEG; n -back; mental workload; multimodal fusion

References