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Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System

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Bibliographic Details
Title: Performance Improvement with Reduced Number of Channels in Motor Imagery BCI System
Authors: Ali Özkahraman, Tamer Ölmez, Zümray Dokur
Source: Sensors, Vol 25, Iss 1, p 120 (2024)
Publisher Information: MDPI AG, 2024.
Publication Year: 2024
Collection: LCC:Chemical technology
Subject Terms: electroencephalography classification, eeg channel reduction, eog noise, motor imagery bci system, Chemical technology, TP1-1185
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.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1424-8220
Relation: https://www.mdpi.com/1424-8220/25/1/120; https://doaj.org/toc/1424-8220
DOI: 10.3390/s25010120
Access URL: https://doaj.org/article/37496774de31445783469eb24ec48721
Accession Number: edsdoj.37496774de31445783469eb24ec48721
Database: Directory of Open Access Journals
Description
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.
ISSN:14248220
DOI:10.3390/s25010120