An EEG Fatigue Detection Method Based on Joint Quantitative Evaluation of Sample and Feature Quality
A technology for fatigue detection and quantitative evaluation, applied in diagnostic recording/measurement, medical science, diagnosis, etc., can solve the problems of lack of reliability in the quality of EEG data, achieve the effect of improving robustness and accuracy, and improving recognition effect
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[0059] The present invention will be further described below in conjunction with accompanying drawing.
[0060] The present invention solves the important problem of mining the important features of EEG signals in fatigue detection based on the following starting point: we believe that in fatigue detection, EEG signals are unsteady signals, and the samples contain more noise. In the learning process, the quality of each sample is characterized and the feature dimension of each sample is selected, so as to select samples and features that are conducive to model training, and a model with better robustness will be obtained. Therefore, we can choose samples and features with better quality for learning, which is of great significance for improving the accuracy of fatigue detection.
[0061] Such as figure 1 and 2 As shown, an EEG fatigue detection method for joint quantitative evaluation of sample and feature quality, the specific steps are as follows:
[0062] Step 1. Using t...
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