Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning
Purpose: Brain-Computer Interfaces (BCI) are advanced systems that enable a direct neural pathway between the human brain and external devices. The importance of BCI is underscored by its profound implications for medical therapeutics, particularly in neurorehabilitation. Materials and Methods:...
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Tehran University of Medical Sciences
2025-03-01
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| Series: | Frontiers in Biomedical Technologies |
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| Online Access: | https://fbt.tums.ac.ir/index.php/fbt/article/view/1050 |
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| author | Ali Ekhlasi Hessam Ahmadi Mohammad Saleh Hoseinzadeh |
| author_facet | Ali Ekhlasi Hessam Ahmadi Mohammad Saleh Hoseinzadeh |
| author_sort | Ali Ekhlasi |
| collection | DOAJ |
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Purpose: Brain-Computer Interfaces (BCI) are advanced systems that enable a direct neural pathway between the human brain and external devices. The importance of BCI is underscored by its profound implications for medical therapeutics, particularly in neurorehabilitation.
Materials and Methods: This study developed an algorithm to detect 8 motion commands for a robot using individuals' EEG signals (Electroencephalogram). These signals were recorded during imagined and expressed commands. The research aimed to identify optimal features for extracting and classifying EEG signals for robot commands and to pinpoint the best EEG channels for a cost-effective, efficient signal acquisition system. Four categories of features, including temporal, frequency, wavelet, and combined features were extracted from the EEG signals. The Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA) were utilized for feature selection.
Results: Findings revealed that wavelet features are most effective for analyzing and classifying EEGs. For imagined commands, optimal features from all channels achieved a 96.3% classification accuracy, while expressed commands reached 96.5%. The frontal and parietal lobes were identified as the prime EEG channels for command detection, achieving accuracies of 91.5% and 86.9% for imagined commands, and 92.7% and 86.1% for expressed commands, respectively. The result also indicated that the brain's midline and left hemisphere (containing the Broca area) outperformed the right hemisphere in classification.
Conclusion: By focusing on the optimal EEG channels, a more cost-effective hardware system can be designed, surpassing the traditional 21-channel system and requiring only 14 electrodes in the frontal and parietal regions.
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| format | Article |
| id | doaj-art-12299a4876dc46afa16ab0c65c6cab69 |
| institution | Kabale University |
| issn | 2345-5837 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Frontiers in Biomedical Technologies |
| spelling | doaj-art-12299a4876dc46afa16ab0c65c6cab692025-08-20T03:53:13ZengTehran University of Medical SciencesFrontiers in Biomedical Technologies2345-58372025-03-0112210.18502/fbt.v12i2.18275Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine LearningAli Ekhlasi0Hessam Ahmadi1Mohammad Saleh Hoseinzadeh2Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran Purpose: Brain-Computer Interfaces (BCI) are advanced systems that enable a direct neural pathway between the human brain and external devices. The importance of BCI is underscored by its profound implications for medical therapeutics, particularly in neurorehabilitation. Materials and Methods: This study developed an algorithm to detect 8 motion commands for a robot using individuals' EEG signals (Electroencephalogram). These signals were recorded during imagined and expressed commands. The research aimed to identify optimal features for extracting and classifying EEG signals for robot commands and to pinpoint the best EEG channels for a cost-effective, efficient signal acquisition system. Four categories of features, including temporal, frequency, wavelet, and combined features were extracted from the EEG signals. The Imperialist Competitive Algorithm (ICA) and Cuckoo Optimization Algorithm (COA) were utilized for feature selection. Results: Findings revealed that wavelet features are most effective for analyzing and classifying EEGs. For imagined commands, optimal features from all channels achieved a 96.3% classification accuracy, while expressed commands reached 96.5%. The frontal and parietal lobes were identified as the prime EEG channels for command detection, achieving accuracies of 91.5% and 86.9% for imagined commands, and 92.7% and 86.1% for expressed commands, respectively. The result also indicated that the brain's midline and left hemisphere (containing the Broca area) outperformed the right hemisphere in classification. Conclusion: By focusing on the optimal EEG channels, a more cost-effective hardware system can be designed, surpassing the traditional 21-channel system and requiring only 14 electrodes in the frontal and parietal regions. https://fbt.tums.ac.ir/index.php/fbt/article/view/1050Brain-Computer Interface (BCI)Robot ControllingBrain RegionsEEGEvolutionary Optimization Algorithms |
| spellingShingle | Ali Ekhlasi Hessam Ahmadi Mohammad Saleh Hoseinzadeh Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning Frontiers in Biomedical Technologies Brain-Computer Interface (BCI) Robot Controlling Brain Regions EEG Evolutionary Optimization Algorithms |
| title | Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning |
| title_full | Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning |
| title_fullStr | Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning |
| title_full_unstemmed | Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning |
| title_short | Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning |
| title_sort | investigating optimal eeg channels and features for brain computer interfaces an exploration using evolutionary algorithms and machine learning |
| topic | Brain-Computer Interface (BCI) Robot Controlling Brain Regions EEG Evolutionary Optimization Algorithms |
| url | https://fbt.tums.ac.ir/index.php/fbt/article/view/1050 |
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