Journal article
Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy
Brain computer interfaces (Abingdon, England), v 4(3), pp 175-185
03 Jul 2017
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy
- Creators
- Yichuan Liu - Cognitive Neuroengineering and Quantitative Experimental Research (CONQUER) Collaborative, Drexel UniversityHasan Ayaz - The Division of General Pediatrics, Children's Hospital of PhiladelphiaPatricia A Shewokis - Nutrition Sciences Department, College of Nursing and Health Professions, Drexel University
- Publication Details
- Brain computer interfaces (Abingdon, England), v 4(3), pp 175-185
- Publisher
- Taylor & Francis
- Grant note
- IIS: 1064871 / National Science Foundation (10.13039/100000001)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems; Nutrition Sciences
- Web of Science ID
- WOS:000424085000004
- Scopus ID
- 2-s2.0-85048091581
- Other Identifier
- 991014877717604721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Biomedical
- Neurosciences