FINE-GRAINED AND MULTI-SCALE MOTIF FEATURES FOR CROSS-SUBJECT MENTAL WORKLOAD ASSESSMENT USING BI-LSTM

• Published in: Journal of mechanics in medicine and biology Vol. 21; no. 5; p. 2140020
• Main Authors: SHAO, SHILIANG, WANG, TING, SONG, CHUNHE, SU, YUN, WANG, YONGLIANG, YAO, CHEN
• Format: Journal Article
• Language: English
• Published: Singapore World Scientific Publishing Company 01.06.2021; World Scientific Publishing Co. Pte., Ltd
• Subjects: Adaptive algorithms; Decomposition; Electroencephalography; Empirical analysis; Feature extraction; Machine learning; Workload; Workloads; Bi-LSTM; cross-subject; Mental workload; electroencephalogram (EEG); motif structure

Mental workload (MW) assessment is crucial for understanding human mental state. Cross-subject MW analysis based on electroencephalogram (EEG) signals is an important way. In this paper, a fine-grained and multi-scale motif (FGMSM) features extraction method is proposed, and the proposed features to...