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<p>Decoding multiclass motor imagery electroencephalography (MI-EEG) data is crucial for brain–computer interface (BCI) applications. This study proposes CCLNet, a novel and high-performing approach for classifying MI-EEG data. CCLNet achieves this performance through two key innovations. First, it introduces a novel common spatial pattern (CSP)-based feature extraction method specifically designed for multiclass MI-EEG data. This method extracts informative features by identifying spatial patterns that maximize the variance between different MI-EEG classes. Second, CCLNet employs a deep learning model with a convolutional neural network and long short-term memory (CNN-LSTM) architecture. The CNN component is employed to extract features to learn complex spatial patterns within the data, while the LSTM captures the temporal dependencies present in the MI-EEG data. By combining these innovations, CCLNet achieves superior classification accuracy compared to state-of-the-art methods. The effectiveness of CCLNet was evaluated using within-subject and cross-subject approaches on two separate datasets: BCI Competition IV-2a and HGD. On BCI Competition IV-2a, CCLNet achieved impressive accuracy, reaching 95.87% and 97.08% for within-subject and cross-subject scenarios, respectively. Furthermore, CCLNet demonstrated exceptional performance on the HGD dataset, achieving an accuracy of 98.56%. These outstanding results highlight CCLNet’s potential for real-world applications, particularly in advancing assistive technologies for individuals with motor disabilities. </p>

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CCLNet: multiclass motor imagery EEG decoding through extended common spatial patterns and CNN-LSTM hybrid network

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • Kamal Singh,
  • Nitin Singha,
  • Swati Bhalaik

摘要

Decoding multiclass motor imagery electroencephalography (MI-EEG) data is crucial for brain–computer interface (BCI) applications. This study proposes CCLNet, a novel and high-performing approach for classifying MI-EEG data. CCLNet achieves this performance through two key innovations. First, it introduces a novel common spatial pattern (CSP)-based feature extraction method specifically designed for multiclass MI-EEG data. This method extracts informative features by identifying spatial patterns that maximize the variance between different MI-EEG classes. Second, CCLNet employs a deep learning model with a convolutional neural network and long short-term memory (CNN-LSTM) architecture. The CNN component is employed to extract features to learn complex spatial patterns within the data, while the LSTM captures the temporal dependencies present in the MI-EEG data. By combining these innovations, CCLNet achieves superior classification accuracy compared to state-of-the-art methods. The effectiveness of CCLNet was evaluated using within-subject and cross-subject approaches on two separate datasets: BCI Competition IV-2a and HGD. On BCI Competition IV-2a, CCLNet achieved impressive accuracy, reaching 95.87% and 97.08% for within-subject and cross-subject scenarios, respectively. Furthermore, CCLNet demonstrated exceptional performance on the HGD dataset, achieving an accuracy of 98.56%. These outstanding results highlight CCLNet’s potential for real-world applications, particularly in advancing assistive technologies for individuals with motor disabilities.