Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery
The electroencephalogram (EEG) is one of the most popular neurophysiological methods in neuroscience. Scalp EEG measurements are obtained using various numbers of channels for both clinical and research applications. This pilot study explores the effect of EEG channel count on motor imagery classifi...
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MDPI AG
2025-06-01
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| Series: | Brain Sciences |
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| Online Access: | https://www.mdpi.com/2076-3425/15/7/685 |
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| author | Mustafa Yazıcı Mustafa Ulutaş Mukadder Okuyan |
| author_facet | Mustafa Yazıcı Mustafa Ulutaş Mukadder Okuyan |
| author_sort | Mustafa Yazıcı |
| collection | DOAJ |
| description | The electroencephalogram (EEG) is one of the most popular neurophysiological methods in neuroscience. Scalp EEG measurements are obtained using various numbers of channels for both clinical and research applications. This pilot study explores the effect of EEG channel count on motor imagery classification using source analysis in brain–computer interface (BCI) applications. Different channel configurations are employed to evaluate classification performance. This study focuses on mu band signals, which are sensitive to motor imagery-related EEG changes. Common spatial patterns are utilized as a spatiotemporal filter to extract signal components relevant to the right hand and right foot extremities. Classification accuracies are obtained using configurations with 19, 30, 61, and 118 electrodes to determine the optimal number of electrodes in motor imagery studies. Experiments are conducted on the BCI Competition III Dataset Iva. The 19-channel configuration yields lower classification accuracy when compared to the others. The results from 118 channels are better than those from 19 channels but not as good as those from 30 and 61 channels. The best results are achieved when 61 channels are utilized. The average accuracy values are 83.63% with 19 channels, increasing to 84.70% with 30 channels, 84.73% with 61 channels, and decreasing to 83.95% when 118 channels are used. |
| format | Article |
| id | doaj-art-3c519cdd52fc4ee3880d7ff7c0a07b28 |
| institution | DOAJ |
| issn | 2076-3425 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-3c519cdd52fc4ee3880d7ff7c0a07b282025-08-20T02:45:45ZengMDPI AGBrain Sciences2076-34252025-06-0115768510.3390/brainsci15070685Effect of EEG Electrode Numbers on Source Estimation in Motor ImageryMustafa Yazıcı0Mustafa Ulutaş1Mukadder Okuyan2Department of Computer Engineering, Faculty of Engineering, Karadeniz Technical University, Trabzon 61080, TürkiyeDepartment of Computer Engineering, Faculty of Engineering, Karadeniz Technical University, Trabzon 61080, TürkiyeDepartment of Physiology, Faculty of Medicine, Karadeniz Technical University, Trabzon 61080, TürkiyeThe electroencephalogram (EEG) is one of the most popular neurophysiological methods in neuroscience. Scalp EEG measurements are obtained using various numbers of channels for both clinical and research applications. This pilot study explores the effect of EEG channel count on motor imagery classification using source analysis in brain–computer interface (BCI) applications. Different channel configurations are employed to evaluate classification performance. This study focuses on mu band signals, which are sensitive to motor imagery-related EEG changes. Common spatial patterns are utilized as a spatiotemporal filter to extract signal components relevant to the right hand and right foot extremities. Classification accuracies are obtained using configurations with 19, 30, 61, and 118 electrodes to determine the optimal number of electrodes in motor imagery studies. Experiments are conducted on the BCI Competition III Dataset Iva. The 19-channel configuration yields lower classification accuracy when compared to the others. The results from 118 channels are better than those from 19 channels but not as good as those from 30 and 61 channels. The best results are achieved when 61 channels are utilized. The average accuracy values are 83.63% with 19 channels, increasing to 84.70% with 30 channels, 84.73% with 61 channels, and decreasing to 83.95% when 118 channels are used.https://www.mdpi.com/2076-3425/15/7/685motor imageryEEGelectrode numberbrain–computer interface |
| spellingShingle | Mustafa Yazıcı Mustafa Ulutaş Mukadder Okuyan Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery Brain Sciences motor imagery EEG electrode number brain–computer interface |
| title | Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery |
| title_full | Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery |
| title_fullStr | Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery |
| title_full_unstemmed | Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery |
| title_short | Effect of EEG Electrode Numbers on Source Estimation in Motor Imagery |
| title_sort | effect of eeg electrode numbers on source estimation in motor imagery |
| topic | motor imagery EEG electrode number brain–computer interface |
| url | https://www.mdpi.com/2076-3425/15/7/685 |
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