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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 \(MIC=4\).

References

  1. Maglione A, Borghini G, Aricò P, Borgia F, Graziani I, Colosimo A, Kong W, Vecchiato G, Babiloni F (2014) Evaluation of the workload and drowsiness during car driving by using high resolution eeg activity and neurophysiologic indices. In: 2014 36th annual international conference of the IEEE engineering in medicine and biology society, pp 6238–6241 . https://doi.org/10.1109/EMBC.2014.6945054

  2. Blankertz B, Muller K-R, Krusienski DJ, Schalk G, Wolpaw JR, Schlogl A, Pfurtscheller G, Millan JR, Schroder M, Birbaumer N (2006) The BCI competition iii: validating alternative approaches to actual BCI problems. IEEE Trans Neural Syst Rehabil Eng 14(2):153–159. https://doi.org/10.1109/TNSRE.2006.875642

    Article  Google Scholar 

  3. Lotte F, Guan C (2010) Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng 58(2):355–362. https://doi.org/10.1109/TBME.2010.2082539

    Article  Google Scholar 

  4. Mishuhina V, Jiang X (2021) Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interface. Pattern Recognit 115:107918. https://doi.org/10.1016/j.patcog.2021.107918

    Article  Google Scholar 

  5. Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441–446. https://doi.org/10.1109/86.895946

    Article  Google Scholar 

  6. Koles ZJ, Lazar MS, Zhou SZ (1990) Spatial patterns underlying population differences in the background EEG. Brain Topogr 2(4):275–284. https://doi.org/10.1007/BF01129656

    Article  Google Scholar 

  7. Meng J, Liu G, Huang G, Zhu X (2009) Automated selecting subset of channels based on CSP in motor imagery brain-computer interface system. In: 2009 IEEE international conference on robotics and biomimetics (ROBIO), pp 2290–2294 . https://doi.org/10.1109/ROBIO.2009.5420462

  8. Mishuhina V, Jiang X (2018) Feature weighting and regularization of common spatial patterns in EEG-based motor imagery BCI. IEEE Signal Process Lett 25(6):783–787. https://doi.org/10.1109/LSP.2018.2823683

    Article  Google Scholar 

  9. Parra LC, Spence CD, Gerson AD, Sajda P (2005) Recipes for the linear analysis of EEG. Neuroimage 28(2):326–341. https://doi.org/10.1016/j.neuroimage.2005.05.032

    Article  Google Scholar 

  10. Wang Y, Gao S, Gao X (2006) Common spatial pattern method for channel selection in motor imagery based brain-computer interface. In: 2005 IEEE engineering in medicine and biology 27th annual conference, pp 5392–5395. https://doi.org/10.3389/fnhum.2022.880304

  11. Grosse-Wentrup M, Buss M (2008) Multiclass common spatial patterns and information theoretic feature extraction. IEEE Trans Biomed Eng 55(8):1991–2000. https://doi.org/10.1109/TBME.2008.921154

    Article  Google Scholar 

  12. Lu H, Plataniotis KN, Venetsanopoulos AN (2009) Regularized common spatial patterns with generic learning for eeg signal classification. In: 2009 annual international conference of the IEEE engineering in medicine and biology society, pp 6599–6602 . https://doi.org/10.1109/IEMBS.2005.1616947

  13. Kang H, Nam Y, Choi S (2009) Composite common spatial pattern for subject-to-subject transfer. IEEE Signal Process Lett 16(8):683–686. https://doi.org/10.1109/LSP.2009.2022557

    Article  Google Scholar 

  14. Arvaneh M, Guan C, Ang KK, Quek C (2011) Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE Trans Biomed Eng 58(6):1865–1873. https://doi.org/10.1109/TBME.2011.2131142

    Article  Google Scholar 

  15. Feng JK, Jin J, Daly I, Zhou J, Niu Y, Wang X, Cichocki A (2019) An optimized channel selection method based on multifrequency CSP-rank for motor imagery-based BCI system. Comput Intell Neurosci. https://doi.org/10.1155/2019/8068357

    Article  Google Scholar 

  16. Li C, Zhou W, Liu G, Zhang Y, Geng M, Liu Z, Wang S, Shang W (2021) Seizure onset detection using empirical mode decomposition and common spatial pattern. IEEE Trans Neural Syst Rehabil Eng 29:458–467. https://doi.org/10.1109/TNSRE.2021.3055276

    Article  Google Scholar 

  17. Sun J, Wei M, Luo N, Li Z, Wang H (2022) Euler common spatial patterns for EEG classification. Med Biol Eng Comput. https://doi.org/10.1007/s11517-021-02488-7

    Article  Google Scholar 

  18. Borghini G, Vecchiato G, Toppi J, Astolfi L, Maglione A, Isabella R, Caltagirone C, Kong W, Wei D, Zhou Z et al (2012) Assessment of mental fatigue during car driving by using high resolution eeg activity and neurophysiologic indices. In: 2012 annual international conference of the IEEE engineering in medicine and biology society, pp 6442–6445 . https://doi.org/10.1109/EMBC.2012.6347469

  19. Chin ZY, Ang KK, Wang C, Guan C, Zhang H (2009) Multi-class filter bank common spatial pattern for four-class motor imagery BCI. In: 2009 annual international conference of the IEEE engineering in medicine and biology society, pp 571–574 . https://doi.org/10.1109/IEMBS.2009.5332383

  20. Hope RM, Wang Z, Wang Z, Ji Q, Gray WD (2011) Workload classification across subjects using EEG. In: Proceedings of the human factors and ergonomics society annual meeting, vol 55, pp 202–206. https://doi.org/10.1177/1071181311551042

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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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