Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning
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Abstract
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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6- MM Rashid, Mohammad Yeakub Ali, and Saeed Sailan, "Service robot using voice recognition." in AIP Conference Proceedings, (2023), Vol. 2643 (No. 1): AIP Publishing.
7- Nicolas Viniegra and Jorge Ierache, "Control of a Robotic Hand Through Voice Commands and Biosignals." in Iberoamerican Workshop on Human-Computer Interaction, (2021): Springer, pp. 32-43.
8- Cihan Uyanik, Muhammad Ahmed Khan, Rig Das, John Paulin Hansen, and Sadasivan Puthusserypady, "Brainy home: a virtual smart home and wheelchair control application powered by brain computer interface." in 15th International Conference on Biomedical Electronics and Devices, (2022): SCITEPRESS Digital Library, pp. 134-41.
9- Olga Maslova et al., "Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review." Frontiers in Human Neuroscience, Vol. 17(2023).
10- Sichao Liu, Lihui Wang, and Robert X Gao, "Cognitive neuroscience and robotics: Advancements and future research directions." Robotics and Computer-Integrated Manufacturing, Vol. 85p. 102610, (2024).
11- Guangyi Zhang, Vandad Davoodnia, Alireza Sepas-Moghaddam, Yaoxue Zhang, and Ali Etemad, "Classification of hand movements from EEG using a deep attention-based LSTM network." IEEE Sensors Journal, Vol. 20 (No. 6), pp. 3113-22, (2019).
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13- Ali Ekhlasi, Ali Motie Nasrabadi, and Mohammadreza Mohammadi, "Classification of the children with ADHD and healthy children based on the directed phase transfer entropy of EEG signals." Frontiers in Biomedical Technologies, (2021).
14- Wenqie Huang, Guanghui Yan, Wenwen Chang, Yuchan Zhang, and Yueting Yuan, "EEG-based classification combining Bayesian convolutional neural networks with recurrence plot for motor movement/imagery." Pattern Recognition, Vol. 144p. 109838, (2023).
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21- Asrari HA, Peters MA. "Diffusion Models and Reinforcement Learning: Novel Pathways to Modeling Decoded fMRI Neurofeedback.", (2024).
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23- Husnaini Azmy and Norlaili Mat Safri, "EEG based BCI using visual imagery task for robot control." Jurnal Teknologi, Vol. 61 (No. 2), p. 10.11113, (2013).
24- Amin Hekmatmanesh, "Investigation of EEG signal processing for rehabilitation robot control." (2019).
25- Bin Shi, Quan Wang, Shuai Yin, Zan Yue, Yaping Huai, and Jing Wang, "A binary harmony search algorithm as channel selection method for motor imagery-based BCI." Neurocomputing, Vol. 443pp. 12-25, (2021).
26- Lin He, Youpan Hu, Yuanqing Li, and Daoli Li, "Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG." Neurocomputing, Vol. 121pp. 423-33, (2013).
27- Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, and Roberto Hornero, "Brain–computer interface channel selection optimization using meta-heuristics and evolutionary algorithms." Applied Soft Computing, Vol. 115p. 108176, (2022).
28- Md Kafiul Islam, Amir Rastegarnia, and Zhi Yang, "Methods for artifact detection and removal from scalp EEG: A review." Neurophysiologie Clinique/Clinical Neurophysiology, Vol. 46 (No. 4-5), pp. 287-305, (2016).
29- P Geethanjali, Y Krishna Mohan, and Jinisha Sen, "Time domain feature extraction and classification of EEG data for brain computer interface." in 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, (2012): IEEE, pp. 1136-39.
30- Swati Vaid, Preeti Singh, and Chamandeep Kaur, "EEG signal analysis for BCI interface: A review." in 2015 fifth international conference on advanced computing & communication technologies, (2015): IEEE, pp. 143-47.
31- Amjed S Al-Fahoum and Ausilah A Al-Fraihat, "Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains." International Scholarly Research Notices, Vol. 2014(2014).
32- Ali Ekhlasi, Hessam Ahmadi, Amir Molavi, Mohammad Saadat Nia, and Ali Motie Nasrabadi, "EEG signal analysis during Ishihara’s test in subjects with normal vision and color vision deficiency." Biomedical Physics & Engineering Express, Vol. 7 (No. 2), p. 025008, (2021).
33- Ashwini Nakate and PD Bahirgonde, "Feature extraction of EEG signal using wavelet transform." International Journal of Computer Applications, Vol. 124 (No. 2), pp. 21-24, (2015).
34- Sridhar Krishnan and Yashodhan Athavale, "Trends in biomedical signal feature extraction." Biomedical Signal Processing and Control, Vol. 43pp. 41-63, (2018).
35- Bashar Rajoub, "Characterization of biomedical signals: Feature engineering and extraction." in Biomedical signal processing and artificial intelligence in healthcare: Elsevier, (2020), pp. 29-50.
36- Bing Xue, Mengjie Zhang, Will N Browne, and Xin Yao, "A survey on evolutionary computation approaches to feature selection." IEEE Transactions on evolutionary computation, Vol. 20 (No. 4), pp. 606-26, (2015).
37- Esmaeil Atashpaz-Gargari and Caro Lucas, "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition." in 2007 IEEE congress on evolutionary computation, (2007): Ieee, pp. 4661-67.
38- Jeng-Shyang Pan, Pei Hu, Václav Snášel, and Shu-Chuan Chu, "A survey on binary metaheuristic algorithms and their engineering applications." Artificial Intelligence Review, Vol. 56 (No. 7), pp. 6101-67, (2023).
39- Ramin Rajabioun, "Cuckoo optimization algorithm." Applied soft computing, Vol. 11 (No. 8), pp. 5508-18, (2011).
40- Sylvain Arlot and Alain Celisse, "A survey of cross-validation procedures for model selection." (2010).
41- Qinghua Hu, Daren Yu, and Zongxia Xie, "Neighborhood classifiers." Expert systems with applications, Vol. 34 (No. 2), pp. 866-76, (2008).
42- Ali Al-Saegh, Shefa A Dawwd, Jassim M %J Biomedical Signal Processing Abdul-Jabbar, and Control, "Deep learning for motor imagery EEG-based classification: A review." Vol. 63p. 102172, (2021).
43- Tarik Bin Shams et al., "EEG-based biometric authentication using machine learning: A comprehensive survey." Vol. 20 (No. 2), pp. 225-41, (2022).
44- David A Moses, Matthew K Leonard, Joseph G Makin, and Edward F %J Nature communications Chang, "Real-time decoding of question-and-answer speech dialogue using human cortical activity." Vol. 10 (No. 1), p. 3096, (2019).
45- Miguel Angrick et al., "Speech synthesis from ECoG using densely connected 3D convolutional neural networks." Vol. 16 (No. 3), p. 036019, (2019).
46- Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H Falk, and Jocelyn %J Journal of neural engineering Faubert, "Deep learning-based electroencephalography analysis: a systematic review." Vol. 16 (No. 5), p. 051001, (2019).
47- Qingsong Ai et al., "Feature extraction of four-class motor imagery EEG signals based on functional brain network." Journal of neural engineering, Vol. 16 (No. 2), p. 026032, (2019).
48- Ming-Ai Li, Yi-Fan Wang, Song-Min Jia, Yan-Jun Sun, and Jin-Fu Yang, "Decoding of motor imagery EEG based on brain source estimation." Neurocomputing, Vol. 339pp. 182-93, (2019).
49- Adeen Flinker and Robert T Knight, "Broca’s area in comprehension and production, insights from intracranial studies in humans." Current opinion in behavioral sciences, Vol. 21pp. 170-75, (2018).
2- Aleksandra Kawala-Sterniuk et al., "Summary of over fifty years with brain-computer interfaces—a review." Brain Sciences, Vol. 11 (No. 1), p. 43, (2021).
3- Christian Deuerlein, Moritz Langer, Julian Sessner, Peter Heß, and Jörg Franke, "Human-robot-interaction using cloud-based speech recognition systems." Procedia Cirp, Vol. 97pp. 130-35, (2021).
4- S Yuvaraj, Abhishek Badholia, P William, K Vengatesan, and Rahul Bibave, "Speech recognition based robotic arm writing." in Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2021, (2022): Springer, pp. 23-33.
5- Sony Kuriakose, MM Harshitha, DG Keerthana, Sadineni Adarsh, and K Harshitha, "Wireless Voice Controlled Robot." in 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), (2023), Vol. 1: IEEE, pp. 189-94.
6- MM Rashid, Mohammad Yeakub Ali, and Saeed Sailan, "Service robot using voice recognition." in AIP Conference Proceedings, (2023), Vol. 2643 (No. 1): AIP Publishing.
7- Nicolas Viniegra and Jorge Ierache, "Control of a Robotic Hand Through Voice Commands and Biosignals." in Iberoamerican Workshop on Human-Computer Interaction, (2021): Springer, pp. 32-43.
8- Cihan Uyanik, Muhammad Ahmed Khan, Rig Das, John Paulin Hansen, and Sadasivan Puthusserypady, "Brainy home: a virtual smart home and wheelchair control application powered by brain computer interface." in 15th International Conference on Biomedical Electronics and Devices, (2022): SCITEPRESS Digital Library, pp. 134-41.
9- Olga Maslova et al., "Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review." Frontiers in Human Neuroscience, Vol. 17(2023).
10- Sichao Liu, Lihui Wang, and Robert X Gao, "Cognitive neuroscience and robotics: Advancements and future research directions." Robotics and Computer-Integrated Manufacturing, Vol. 85p. 102610, (2024).
11- Guangyi Zhang, Vandad Davoodnia, Alireza Sepas-Moghaddam, Yaoxue Zhang, and Ali Etemad, "Classification of hand movements from EEG using a deep attention-based LSTM network." IEEE Sensors Journal, Vol. 20 (No. 6), pp. 3113-22, (2019).
12- Ali Ekhlasi, Ali Motie Nasrabadi, and Mohammadreza Mohammadi, "Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer." Biomedical Engineering/Biomedizinische Technik, Vol. 68 (No. 2), pp. 133-46, (2023).
13- Ali Ekhlasi, Ali Motie Nasrabadi, and Mohammadreza Mohammadi, "Classification of the children with ADHD and healthy children based on the directed phase transfer entropy of EEG signals." Frontiers in Biomedical Technologies, (2021).
14- Wenqie Huang, Guanghui Yan, Wenwen Chang, Yuchan Zhang, and Yueting Yuan, "EEG-based classification combining Bayesian convolutional neural networks with recurrence plot for motor movement/imagery." Pattern Recognition, Vol. 144p. 109838, (2023).
15- Pham Van Huu Thien and Nguyen Ngoc Son, "Electroencephalography-based brain-computer interface using neural networks." TELKOMNIKA (Telecommunication Computing Electronics and Control), Vol. 21 (No. 5), pp. 1068-75, (2023).
16- Cédric Simar, Mathieu Petieau, Anita Cebolla, Axelle Leroy, Gianluca Bontempi, and Guy Cheron, "EEG-based brain-computer interface for alpha speed control of a small robot using the MUSE headband." in 2020 International Joint Conference on Neural Networks (IJCNN), (2020): IEEE, pp. 1-4.
17- Nikolaos Korovesis, Dionisis Kandris, Grigorios Koulouras, and Alex Alexandridis, "Robot motion control via an EEG-based brain–computer interface by using neural networks and alpha brainwaves." Electronics, Vol. 8 (No. 12), p. 1387, (2019).
18- Rihab Bousseta, I El Ouakouak, M Gharbi, and F Regragui, "EEG based brain computer interface for controlling a robot arm movement through thought." Irbm, Vol. 39 (No. 2), pp. 129-35, (2018).
19- Na Dong, Zhiqiang Wu, Wenqi Zhang, Guanrong Chen, and Zhongke Gao, "Intention-prioritized fuzzy fusion control for BCI-based autonomous vehicles." Biomedical Signal Processing and Control, Vol. 87p. 105486, (2024).
20- Orouji S, Taschereau-Dumouchel V, Cortese A, Odegaard B, Cushing C, Cherkaoui M, et al. "Task relevant autoencoding enhances machine learning for human neuroscience." Scientific reports.;15(1):1365, (2025).
21- Asrari HA, Peters MA. "Diffusion Models and Reinforcement Learning: Novel Pathways to Modeling Decoded fMRI Neurofeedback.", (2024).
22- Yizhi Liu, Mahmoud Habibnezhad, and Houtan Jebelli, "Brain-computer interface for hands-free teleoperation of construction robots." Automation in Construction, Vol. 123p. 103523, (2021).
23- Husnaini Azmy and Norlaili Mat Safri, "EEG based BCI using visual imagery task for robot control." Jurnal Teknologi, Vol. 61 (No. 2), p. 10.11113, (2013).
24- Amin Hekmatmanesh, "Investigation of EEG signal processing for rehabilitation robot control." (2019).
25- Bin Shi, Quan Wang, Shuai Yin, Zan Yue, Yaping Huai, and Jing Wang, "A binary harmony search algorithm as channel selection method for motor imagery-based BCI." Neurocomputing, Vol. 443pp. 12-25, (2021).
26- Lin He, Youpan Hu, Yuanqing Li, and Daoli Li, "Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG." Neurocomputing, Vol. 121pp. 423-33, (2013).
27- Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, and Roberto Hornero, "Brain–computer interface channel selection optimization using meta-heuristics and evolutionary algorithms." Applied Soft Computing, Vol. 115p. 108176, (2022).
28- Md Kafiul Islam, Amir Rastegarnia, and Zhi Yang, "Methods for artifact detection and removal from scalp EEG: A review." Neurophysiologie Clinique/Clinical Neurophysiology, Vol. 46 (No. 4-5), pp. 287-305, (2016).
29- P Geethanjali, Y Krishna Mohan, and Jinisha Sen, "Time domain feature extraction and classification of EEG data for brain computer interface." in 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, (2012): IEEE, pp. 1136-39.
30- Swati Vaid, Preeti Singh, and Chamandeep Kaur, "EEG signal analysis for BCI interface: A review." in 2015 fifth international conference on advanced computing & communication technologies, (2015): IEEE, pp. 143-47.
31- Amjed S Al-Fahoum and Ausilah A Al-Fraihat, "Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains." International Scholarly Research Notices, Vol. 2014(2014).
32- Ali Ekhlasi, Hessam Ahmadi, Amir Molavi, Mohammad Saadat Nia, and Ali Motie Nasrabadi, "EEG signal analysis during Ishihara’s test in subjects with normal vision and color vision deficiency." Biomedical Physics & Engineering Express, Vol. 7 (No. 2), p. 025008, (2021).
33- Ashwini Nakate and PD Bahirgonde, "Feature extraction of EEG signal using wavelet transform." International Journal of Computer Applications, Vol. 124 (No. 2), pp. 21-24, (2015).
34- Sridhar Krishnan and Yashodhan Athavale, "Trends in biomedical signal feature extraction." Biomedical Signal Processing and Control, Vol. 43pp. 41-63, (2018).
35- Bashar Rajoub, "Characterization of biomedical signals: Feature engineering and extraction." in Biomedical signal processing and artificial intelligence in healthcare: Elsevier, (2020), pp. 29-50.
36- Bing Xue, Mengjie Zhang, Will N Browne, and Xin Yao, "A survey on evolutionary computation approaches to feature selection." IEEE Transactions on evolutionary computation, Vol. 20 (No. 4), pp. 606-26, (2015).
37- Esmaeil Atashpaz-Gargari and Caro Lucas, "Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition." in 2007 IEEE congress on evolutionary computation, (2007): Ieee, pp. 4661-67.
38- Jeng-Shyang Pan, Pei Hu, Václav Snášel, and Shu-Chuan Chu, "A survey on binary metaheuristic algorithms and their engineering applications." Artificial Intelligence Review, Vol. 56 (No. 7), pp. 6101-67, (2023).
39- Ramin Rajabioun, "Cuckoo optimization algorithm." Applied soft computing, Vol. 11 (No. 8), pp. 5508-18, (2011).
40- Sylvain Arlot and Alain Celisse, "A survey of cross-validation procedures for model selection." (2010).
41- Qinghua Hu, Daren Yu, and Zongxia Xie, "Neighborhood classifiers." Expert systems with applications, Vol. 34 (No. 2), pp. 866-76, (2008).
42- Ali Al-Saegh, Shefa A Dawwd, Jassim M %J Biomedical Signal Processing Abdul-Jabbar, and Control, "Deep learning for motor imagery EEG-based classification: A review." Vol. 63p. 102172, (2021).
43- Tarik Bin Shams et al., "EEG-based biometric authentication using machine learning: A comprehensive survey." Vol. 20 (No. 2), pp. 225-41, (2022).
44- David A Moses, Matthew K Leonard, Joseph G Makin, and Edward F %J Nature communications Chang, "Real-time decoding of question-and-answer speech dialogue using human cortical activity." Vol. 10 (No. 1), p. 3096, (2019).
45- Miguel Angrick et al., "Speech synthesis from ECoG using densely connected 3D convolutional neural networks." Vol. 16 (No. 3), p. 036019, (2019).
46- Yannick Roy, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort, Tiago H Falk, and Jocelyn %J Journal of neural engineering Faubert, "Deep learning-based electroencephalography analysis: a systematic review." Vol. 16 (No. 5), p. 051001, (2019).
47- Qingsong Ai et al., "Feature extraction of four-class motor imagery EEG signals based on functional brain network." Journal of neural engineering, Vol. 16 (No. 2), p. 026032, (2019).
48- Ming-Ai Li, Yi-Fan Wang, Song-Min Jia, Yan-Jun Sun, and Jin-Fu Yang, "Decoding of motor imagery EEG based on brain source estimation." Neurocomputing, Vol. 339pp. 182-93, (2019).
49- Adeen Flinker and Robert T Knight, "Broca’s area in comprehension and production, insights from intracranial studies in humans." Current opinion in behavioral sciences, Vol. 21pp. 170-75, (2018).
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| Issue | Vol 12 No 2 (2025) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v12i2.18275 | |
| Keywords | ||
| Brain-Computer Interface (BCI) Robot Controlling Brain Regions EEG Evolutionary Optimization Algorithms | ||
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How to Cite
1.
Ekhlasi A, Ahmadi H, Hoseinzadeh MS. Investigating Optimal EEG Channels and Features for Brain-Computer Interfaces: An Exploration using Evolutionary Algorithms and Machine Learning. Frontiers Biomed Technol. 2025;12(2):278-291.
