TY - JOUR TI - Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy AU - Liu, Yichuan AU - Ayaz, Hasan AU - Shewokis, Patricia A. T2 - Brain-Computer Interfaces AB - 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 multimodalitybased approach to decode human mental workload levels that may potentially be used for adaptive HCI applications. DA - 2017/07/03/ PY - 2017 DO - 10.1080/2326263X.2017.1304020 DP - DOI.org (Crossref) VL - 4 IS - 3 SP - 175 EP - 185 J2 - Brain-Computer Interfaces LA - en SN - 2326-263X, 2326-2621 UR - https://www.tandfonline.com/doi/full/10.1080/2326263X.2017.1304020 Y2 - 2021/06/01/17:27:20 ER -