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

• Published in: Neural computing & applications Vol. 35; no. 2; pp. 1423 - 1445
• Main Authors: Zeng, Hong, Zakaria, Wael
• Format: Journal Article
• Language: English
• Published: London Springer London 01.01.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Channels; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Customization; Data Mining and Knowledge Discovery; Electroencephalography; Feature extraction; Image Processing and Computer Vision; Original Article; Probability and Statistics in Computer Science; Signal classification; Unified channels selection; Cross-subjects; Common spatial pattern; Channels selection; Subject-by-subject
• DOI: 10.1007/s00521-022-07833-x
• ISSN: 0941-0643

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.