Abstract
Classifying Motor Imaging (MI) Electroencephalogram (EEG) signals is of vital importance for Brain–Computer Interface (BCI) systems, but challenges remain. A key challenge is to reduce the number of channels to improve flexibility, portability, and computational efficiency, especially in multi-class scenarios where more channels are needed for accurate classification. This study demonstrates that combining Electrooculogram (EOG) channels with a reduced set of EEG channels is more effective than relying on a large number of EEG channels alone. EOG channels provide useful information for MI signal classification, countering the notion that they only introduce eye-related noise. The study uses advanced deep learning techniques, including multiple 1D convolution blocks and depthwise-separable convolutions, to optimize classification accuracy. The findings in this study are tested on two datasets: dataset 1, the BCI Competition IV Dataset IIa (4-class MI), and dataset 2, the Weibo dataset (7-class MI). The performance for dataset 1, utilizing 3 EEG and 3 EOG channels (6 channels total), is of 83% accuracy, while dataset 2, with 3 EEG and 2 EOG channels (5 channels total), achieves an accuracy of 61%, demonstrating the effectiveness of the proposed channel reduction method and deep learning model.
Original language | English |
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Article number | 120 |
Journal | Sensors |
Volume | 25 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2025 |
Bibliographical note
Publisher Copyright:© 2024 by the authors.
Keywords
- eeg channel reduction
- electroencephalography classification
- eog noise
- motor imagery bci system