EEG-based physiological feature analysis of expert operators in grinding process

Complex industrial process is often inseparable from human behavior factors. It is helpful to improve intelligence, if we find the physiological feature regularity of the operators and assess their influence factors objectively in the complex industrial process. In this paper, the wavelet entropy (WE) and wavelet time-frequency analysis were used to find the eLectroencephalogram (EEG) features of the operators in a complex industrial process (i.e. the grinding process). We also proposed the wavelet entropy algorithm based on B-spline curve for real-time analysis to find the physiological feature regularity and assess their influence factors objectively.