EEG Feature Analysis of Expert Operators in Grinding Process Control

In the context of systematic optimization and intelligent upgrade of the mineral production, the assessment and quantification of the operators' behavioral factors need to be investigated to further enhance productivity and quality of the products. A real-time analysis method based on the electroencephalography (EEG) characteristics was presented in grinding process. To begin with, the δ, θ, α, and β rhythms in different brain regions were extracted using wavelet decomposition. Then the wavelet entropy can be obtained by calculating the energy sequence distribution of different wavelet coefficient vectors. According to the comparison of the entropy values, the specific brain region was selected. Through wavelet time-frequency analysis, (α+β)/(δ+θ+α+β) was determined as the spectral characteristic. Finally, the results of real-time analysis using B-spline curve and sliding window showed that the physiological indicators can reflect the trend of the granularity curves and assess the operators' influence factors objectively to some extent.