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A new common spatial pattern-based unified channels algorithm for driver’s fatigue EEG signals classification

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Abstract

The common spatial pattern (CSP) algorithm is efficient and accurate for channels selection and features extraction for electroencephalogram (EEG) signals classification. The CSP algorithm is usually applied on a subject-by-subject basis by measuring only intra-subject variations for selecting the most significant channels; we refer to this algorithm as CSP-based customized channels selection (CSP-CC). In practice, deploying the CSP-CC algorithm requires to set up a customized EEG device for each subject separately, which can be very costly. In this paper, we propose a new algorithm, called CSP-based unified channels (CSP-UC), for overcoming the aforementioned difficulties. The aim of the proposed algorithm is to extract unified channels that are valid for any subject; hence, one EEG device can be deployed for all subjects. Moreover, a methodology for developing both binary-class and ternary-class EEG signals classification models using either customized or unified channels is introduced. This methodology is applicable for both subject-by-subject and cross-subjects basis. In ternary-class classification models, the traditional “Max_Vote” method, used for voting the predicted class labels, has been modified to a more accurate method called “Max_Vote_then_Max_Probability.” On a subject-by-subject basis, the experimental results on EEG-based driver’s fatigue dataset have shown that the accuracy of the classification models that are based on the proposed CSP-UC algorithm is slightly lower than that of those based on the CSP-CC algorithm. Nevertheless, the former algorithm is more practical and cost-effective than the latter. But in cross-subjects, the classification models based on the CSP-UC algorithm outperform those based on the CSP-CC algorithm in both accuracy and the number of used channels.

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Notes

  1. We applied the classification model on \(MIC=2,\ 3,\ 4,\ ... \), the best accuracy achieved when \(MIC=4\). This is the reason for choosing \(MIC=4\).

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Acknowledgements

This work was supported by the National Key R &D Program of China with grant no. 2017YFE0118200, NSFC with grant no. 62076083, Fundamental Research Funds for the Provincial Universities of Zhejiang (K209907299001-008), and Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province (2020E10010). The authors specially thank Industrial Neuroscience Lab of University of Rome “La Sapienza” for the support.

Funding

This work was supported by the National Key \( R \& D\) Program of China with grant no. 2017YFE0118200, also supported by NSFC with grant no. 62076083.

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Authors and Affiliations

  1. Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China

    Hong Zeng

  2. School of Computer Science and Technology, Hangzhou, Zhejiang, China

    Hong Zeng

  3. Department of Mathematics, Faculty of Science, Ain Shams University, Cairo, Egypt

    Wael Zakaria

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  1. Hong Zeng
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  2. Wael Zakaria
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Both authors contributed equally to this work.

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Correspondence to Wael Zakaria.

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Zeng, H., Zakaria, W. A new common spatial pattern-based unified channels algorithm for driver’s fatigue EEG signals classification. Neural Comput & Applic 35, 1423–1445 (2023). https://doi.org/10.1007/s00521-022-07833-x

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