Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Liu, Yichuan; Ayaz, Hasan; Shewokis, Patricia A.
• Is Part Of: Brain-computer interfaces. Volume 4:Issue 3(2017)
• Page Start: 175
• Page End: 185
• Publication Date: 2017-07-03
• Subjects: FNIRS -- EEG -- n-back -- mental workload -- multimodal fusion; Brain-computer interfaces -- Periodicals; Neurology -- Periodicals; 616.800285
• DOI: 10.1080/2326263X.2017.1304020
• Languages: English
• ISSNs: 2326-263X

Abstract

Abstract: A 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.