Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System
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...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/1/120 |
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| author | Ali Özkahraman Tamer Ölmez Zümray Dokur |
| author_facet | Ali Özkahraman Tamer Ölmez Zümray Dokur |
| author_sort | Ali Özkahraman |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-37496774de31445783469eb24ec48721 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-37496774de31445783469eb24ec487212025-01-10T13:20:56ZengMDPI AGSensors1424-82202024-12-0125112010.3390/s25010120Performance Improvement with Reduced Number of Channels in Motor Imagery BCI SystemAli Özkahraman0Tamer Ölmez1Zümray Dokur2Department of Electronics and Communication Engineering, Istanbul Technical University, 34467 Istanbul, Istanbul, TurkeyDepartment of Electronics and Communication Engineering, Istanbul Technical University, 34467 Istanbul, Istanbul, TurkeyDepartment of Electronics and Communication Engineering, Istanbul Technical University, 34467 Istanbul, Istanbul, TurkeyClassifying 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.https://www.mdpi.com/1424-8220/25/1/120electroencephalography classificationeeg channel reductioneog noisemotor imagery bci system |
| spellingShingle | Ali Özkahraman Tamer Ölmez Zümray Dokur Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System Sensors electroencephalography classification eeg channel reduction eog noise motor imagery bci system |
| title | Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System |
| title_full | Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System |
| title_fullStr | Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System |
| title_full_unstemmed | Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System |
| title_short | Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System |
| title_sort | performance improvement with reduced number of channels in motor imagery bci system |
| topic | electroencephalography classification eeg channel reduction eog noise motor imagery bci system |
| url | https://www.mdpi.com/1424-8220/25/1/120 |
| work_keys_str_mv | AT aliozkahraman performanceimprovementwithreducednumberofchannelsinmotorimagerybcisystem AT tamerolmez performanceimprovementwithreducednumberofchannelsinmotorimagerybcisystem AT zumraydokur performanceimprovementwithreducednumberofchannelsinmotorimagerybcisystem |