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Extracting and supplementing method for EEG signal in manufacturing workshop based on deep learning of time–frequency correlation

Author

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  • Bin Ren

    (Shanghai University)

  • Yunjie Pan

    (Shanghai University)

Abstract

The quality of EEG signals is extremely important for brain–computer interface systems, especially in complex environments such as manufacturing floors. At present, there are few studies on the recovery methods of damaged EEG signals. Therefore, a TFCMI–CNN–LSTM hybrid model of time–frequency correlation analysis combined with deep learning is proposed to predict the missing data of EEG signals. We designed a brain fatigue experiment in a noisy environment to simulate the actual work situation of workers in the workshop, and verified the performance of the prediction model combined with the public EEG dataset BCI Competition IV 2a. The prediction results of the data set BCI Competition IV 2a show that the average RMSE between the prediction results of the TFCMI–CNN–LSTM model and the true value is 3.512, the MAE is 2.787, the Spearman Rank is 0.828, which can effectively restore abnormal EEG signals. This paper studies the importance of the CNN module in the model. The results show that the CNN module can greatly reduce the time spent in model training, and is more suitable for real-time EEG signal acquisition systems.

Suggested Citation

  • Bin Ren & Yunjie Pan, 2023. "Extracting and supplementing method for EEG signal in manufacturing workshop based on deep learning of time–frequency correlation," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3179-3196, October.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:7:d:10.1007_s10845-022-01997-y
    DOI: 10.1007/s10845-022-01997-y
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    References listed on IDEAS

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    1. Zhang, Wen-Yao & Wei, Zong-Wen & Wang, Bing-Hong & Han, Xiao-Pu, 2016. "Measuring mixing patterns in complex networks by Spearman rank correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 440-450.
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