Fatigue feature extraction and classification algorithm of forehead single-channel electroencephalography signals

Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is propos...

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Bibliographic Details
Published inSheng wu yi xue gong cheng xue za zhi Vol. 41; no. 4; p. 732
Main Authors Yang, Huizhou, Liu, Yunfei, Xia, Lijuan
Format Journal Article
LanguageChinese
Published China 25.08.2024
Subjects
Algorithms
Electroencephalography - methods
Fatigue - diagnosis
Fatigue - physiopathology
Forehead
Humans
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio
Driving fatigue detection
Feature extraction
Supervised contrastive learning
Single-channel electroencephalography signal
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Summary:Aiming at the problem that the feature extraction ability of forehead single-channel electroencephalography (EEG) signals is insufficient, which leads to decreased fatigue detection accuracy, a fatigue feature extraction and classification algorithm based on supervised contrastive learning is proposed. Firstly, the raw signals are filtered by empirical modal decomposition to improve the signal-to-noise ratio. Secondly, considering the limitation of the one-dimensional signal in information expression, overlapping sampling is used to transform the signal into a two-dimensional structure, and simultaneously express the short-term and long-term changes of the signal. The feature extraction network is constructed by depthwise separable convolution to accelerate model operation. Finally, the model is globally optimized by combining the supervised contrastive loss and the mean square error loss. Experiments show that the average accuracy of the algorithm for classifying three fatigue states can reach 75.80%, which
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content type line 23
ISSN:1001-5515
DOI:10.7507/1001-5515.202312026