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<p>A brain-computer interface (BCI) is a system that communicates between the brain and an external device. The electroencephalogram (EEG) is the most favourable tool for extracting neural signals from the brain. Motor Imagery (MI) based BCI with EEG signals is an active BCI paradigm. The performance of MI-based BCI is easily affected by noise and redundant information. To decrease noisy and redundant information and increase the spatial resolution of the EEG signals, a multichannel EEG-based BCI system is used. However, high-dimensional data from multichannel BCI systems has a serious impact on the classification performance. Therefore, for better classification performance of EEG-based BCI systems, channel selection methods are used. Generally, many traditional signal processing techniques have been used for feature-based channel selection. However, such type estimation discards the phase relationship among frequency components. To solve this problem, a bispectrum based channel selection technique is used to overcome the drawback of the power spectrum. It effectively provides the frequency domain information of MI-related brain activities. Therefore, in this study, a bispectrum-based channel selection algorithm is proposed for the MI-based BCI system. The most relevant channels from bispectrum analysis are selected from bispectrum analysis using a set-based integer-coded fuzzy granular evolutionary algorithm. The features are extracted from the selected channels using wavelet scattering transform. Finally, the experiments are tested on multiple classifiers and the best performance is obtained using the support vector machine classifier. The other classifiers also attained significant results using a minimum number of EEG channels. The proposed approach, integrating bispectrum-based channel selection, SIFE, and WST, offers a significant improvement in MI-EEG classification accuracy. By reducing irrelevant features and optimizing channel selection, the framework achieves improved performance with reduced computational complexity, demonstrating its potential as a robust tool for real-time BCI applications.</p>

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BSPKTM-SIFE-WST: bispectrum based channel selection using set-based-integer-coded fuzzy granular evolutionary algorithm and wavelet scattering transform for motor imagery EEG classification

  • 期刊论文

  • 发表日期：

    2025年2月4日

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  • Vikram Singh Kardam,
  • Sachin Taran,
  • Anukul Pandey

摘要

A brain-computer interface (BCI) is a system that communicates between the brain and an external device. The electroencephalogram (EEG) is the most favourable tool for extracting neural signals from the brain. Motor Imagery (MI) based BCI with EEG signals is an active BCI paradigm. The performance of MI-based BCI is easily affected by noise and redundant information. To decrease noisy and redundant information and increase the spatial resolution of the EEG signals, a multichannel EEG-based BCI system is used. However, high-dimensional data from multichannel BCI systems has a serious impact on the classification performance. Therefore, for better classification performance of EEG-based BCI systems, channel selection methods are used. Generally, many traditional signal processing techniques have been used for feature-based channel selection. However, such type estimation discards the phase relationship among frequency components. To solve this problem, a bispectrum based channel selection technique is used to overcome the drawback of the power spectrum. It effectively provides the frequency domain information of MI-related brain activities. Therefore, in this study, a bispectrum-based channel selection algorithm is proposed for the MI-based BCI system. The most relevant channels from bispectrum analysis are selected from bispectrum analysis using a set-based integer-coded fuzzy granular evolutionary algorithm. The features are extracted from the selected channels using wavelet scattering transform. Finally, the experiments are tested on multiple classifiers and the best performance is obtained using the support vector machine classifier. The other classifiers also attained significant results using a minimum number of EEG channels. The proposed approach, integrating bispectrum-based channel selection, SIFE, and WST, offers a significant improvement in MI-EEG classification accuracy. By reducing irrelevant features and optimizing channel selection, the framework achieves improved performance with reduced computational complexity, demonstrating its potential as a robust tool for real-time BCI applications.