Mental Fatigue Detection of Construction Equipment Operators Based on EEG Collected by a Novel Valve-Type Semidry Electrode Using Deep Residual Shrinkage Networks Under Real Construction Environment

• Published in: IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 14
• Main Authors: Wang, Fuwang, Chen, Daping, Zhang, Xiaolei
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Classification algorithms; Construction equipment; Construction equipment operators; Data acquisition; deep residual shrinkage network (DRSN); Electrodes; Electroencephalography; electroencephalography (EEG); Fatigue; Feature recognition; Impedance; mental fatigue; Noise measurement; novel valve-type semidry electrode; Operators (personnel); Signal classification; Signal quality; Skin; Springs

Dry electrodes used for electroencephalography (EEG) signals acquisition are of good portability. However, the large impedance of the dry electrode often results in poor signal acquisition quality. Meanwhile, the preparation of wet electrodes in experiments is complex, and replenishing the conductiv...