Brain-Computer Interface (BCI) is a computerized system that gathers, analyzes, and translates neural signals into commands, which are then transmitted to an output device to perform certain tasks. One of the most difficult parts of the BCI Motor Imagery-Electroencephalogram (MI-EEG) based system is the Classification Accuracy (CA). In order to get accurate classification, efficient and rapid features extraction is required for developing a successful MI-EEG classification model. In this article, the Motor Imagery (MI) of Left-Hand (LH) and Right-Hand (RH) actions is recognized using the Weight Features Function (WFF) that transforms initial features into more discriminant features to feed a Support Vector Machine (SVM) classifier. Appropriate weights were chosen by the Genetic Algorithm (GA) optimization method. Applying optimized WFF to four different datasets (IIIb from BCI competition III, III from BCI competition II, 2b from BCI competition IV, and Open Brain- Machine Interface (OpenBMI) dataset) made significant improvements in the CA for all studied datasets. Before using the WFF technique, the initial CA for the four datasets was 90.1%, 95.71%, 86.73%, and 83.83%. After applying the WFF technique, the CA is improved and achieves 96.1%, 100%, 94.2%, and 88.70% respectively.

[1] Altaheri H., Muhammad G., Alsulaiman M., Amin S., and et al., “Deep Learning Techniques for Classification of Electroencephalogram (EEG) Motor Imagery (MI) Signals: A Review,” Neural Computing and Applications, vol. 35, pp. 14681- 14722, 2023. https://doi.org/10.1007/s00521-021- 06352-5

[2] Bashashati H., Ward R., Birch G., and Bashashati A., “Comparing Different Classifiers in Sensory Motor Brain Computer Interfaces,” PLoS ONE, vol. 10, no. 6, pp. 1-17, 2015. https://doi.org/10.1371/journal.pone.0129435

[3] Bhattacharyya S., Khasnobish A., Chatterjee S., Konar A., and Tibarewala D., “Performance Analysis of LDA, QDA and KNN Algorithms in Left-Right Limb Movement Classification from EEG Data,” in Proceedings of the International Conference on Systems in Medicine and Biology, Kharagpur, pp. 126-131, 2010. https://ieeexplore.ieee.org/document/5735358

[4] Blankertz B., Muller K., Curio G., Vaughan T., and et al., “The BCI Competition 2003: Progress and Perspectives in Detection and Discrimination of EEG Single Trials,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1044- 1051, 2004. https://pubmed.ncbi.nlm.nih.gov/15188876/

[5] Blankertz B., Muller K., Krusienski D., Schalk G., and et al., “The BCI Competition III: Validating Alternative Approaches to Actual BCI Problems,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 153-159, 2006. https://doi.org/10.1109/TNSRE.2006.875642

[6] Brodu N., Lotte F., and Lecuyer A., “Comparative Study of Band-Power Extraction Techniques for Motor Imagery Classification,” in Proceedings of the IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Paris, pp. 1-6, 2011. https://ieeexplore.ieee.org/document/5952105

[7] Brodu N., Lotte F., and Lecuyer A., “Exploring Two Novel Features for EEG-based Brain- Computer Interfaces: Multifractal Cumulants and Predictive Complexity,” Neurocomputing, vol. 79, pp. 87-94, 2012. https://doi.org/10.1016/j.neucom.2011.10.010

[8] Buskila Y., Bellot-Saez A., and Morley J., Enhancing Motor Imagery EEG Classification Accuracy Using Weight Features Function 1151 “Generating Brain Waves, the Power of Astrocytes,” Frontiers in Neuroscience, vol. 13, pp. 1-10, 2019. DOI: 10.3389/fnins.2019.01125

[9] Chen C., Yu X., Belkacem A., Lu L., and et al., “EEG-based Anxious States Classification Using Affective BCI-based Closed Neurofeedback System,” Journal of Medical and Biological Engineering, vol. 41, pp. 155-164, 2021. https://doi.org/10.1007/s40846-020-00596-7

[10] Chen W., Wang X., and Zhang T., “Research of Discrimination between Left and Right Hand Motor Imagery EEG Patterns Based on Tunable Q-Factor Wavelet Transform,” Journal of Electronics and Information Technology, vol. 41, no. 3, pp. 530-536, 2019. https://www.jeit.ac.cn/en/article/doi/10.11999/JEI T171191

[11] Chowdary M., Anitha J., and Hemanth D., “Emotion Recognition from EEG Signals Using Recurrent Neural Networks,” Electronics, vol. 11, no. 15, pp. 1-20, 2022. https://doi.org/10.3390/electronics11152387

[12] Coyle D., Prasad G., and McGinnity T., “A Time- Frequency Approach to Feature Extraction for a Brain-Computer Interface with a Comparative Analysis of Performance Measures,” EURASIP Journal on Advances in Signal Processing, vol. 19, pp. 3141-3151, 2005. https://doi.org/10.1155/ASP.2005.3141

[13] Dagdevir E. and Tokmakci M., “Optimization of Preprocessing Stage in EEG Based BCI Systems in Terms of Accuracy and Timing Cost,” Biomedical Signal Processing and Control, vol. 67, pp. 102548, 2021. https://doi.org/10.1016/j.bspc.2021.102548

[14] Han Y., Wang B., Luo J., Li L., and Li X., “A Classification Method for EEG Motor Imagery Signals Based on Parallel Convolutional Neural Network,” Biomedical Signal Processing and Control, vol. 71, pp. 103190, 2021. https://doi.org/10.1016/j.bspc.2021.103190

[15] Jang T., Kim B., Yang Y., Lim W., and Oh D., “Motor-Imagery EEG Signal Classification Using Position Matching and Vector Quantisation,” International Journal of Telemedicine and Clinical Practices, vol. 1, no. 4, pp. 306-313, 2016. https://doi.org/10.1504/IJTMCP.2016.078426

[16] Kant P., Laskar S., Hazarika J., and Mahamune R., “CWT Based Transfer Learning for Motor Imagery Classification for Brain Computer Interfaces,” Journal of Neuroscience Methods, vol. 345, pp. 108886, 2020. https://doi.org/10.1016/j.jneumeth.2020.108886

[17] Khasnobish A., Bhattacharyya S., Konar A., and Tibarewala D., “K-Nearest Neighbor Classification of Left-Right Limb Movement Using EEG Data,” in Proceedings of the 2nd International Conference on Biomedical Engineering and Assistive Technologies, Punjab, pp. 1-6, 2010. https://www.academia.edu/20886132/K_Nearest_ neighbor_classification_of_left_right_limb_move ment_using_EEG_data

[18] Lee D., Park S., and Lee S., “Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-based Combined Feature Vectors and Gaussian Mixture Model-Supervectors,” Sensors, vol. 17, no. 10, pp. 1-18, 2017. https://doi.org/10.3390/s17102282

[19] Lee M., Kwon O., Kim Y., Kim H., and et al., “Supporting Data for EEG Dataset and OpenBMI Toolbox for three BCI Paradigms: An Investigation into BCI Illiteracy,” GigaScience, vol. 8, no. 5, pp. 1-16, 2019. https://doi.org/10.1093/gigascience/giz002

[20] Leeb R., Brunner C., Muller-Putz G., Schlogl A., and Pfurtscheller G., “BCI Competition 2008- Graz Data Set B,” Graz University of Technology, vol. 16, pp. 1-6, 2008. https://www.bbci.de/competition/iv/desc_2b.pdf

[21] Liu A., Chen K., Liu Q., Ai Q., and et al., “Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata,” Sensors, vol. 17, no. 11, pp. 1-15, 2017. https://doi.org/10.3390/s17112576

[22] Liu C., Zhao H., Li C., and Wang H., “CSP/SVM- based EEG Classification of Imagined Hand Movements,” Journal of Northeast University, vol. 31, no. 8, pp. 1098-1101, 2010. https://xuebao.neu.edu.cn/natural/EN/Y2010/V31 /I8/1098

[23] Liu X., Xiong S., Wang X., Liang T., and et al., “A Compact Multi-Branch 1D Convolutional Neural Network for EEG-based Motor Imagery Classification,” Biomedical Signal Processing and Control, vol. 81, pp. 104456, 2023. https://doi.org/10.1016/j.bspc.2022.104456

[24] Lotte F., Lecuyer A., Lamarche F., Arnaldi B.., “Studying the Use of Fuzzy Inference Systems for Motor Imagery Classification,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 15, no. 2, pp. 322-324, 2007. https://doi.org/10.1109/TNSRE.2007.897032

[25] Lu N., Li T., Ren X., and Miao H., “A Deep Learning Scheme for motor imagery Classification Based on Restricted Boltzmann Machines,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp. 566-576, 2017. https://ieeexplore.ieee.org/document/7546909

[26] Malan N. and Sharma S., “Motor Imagery EEG Spectral-Spatial Feature Optimization Using Dual-Tree Complex Wavelet and Neighbourhood Component Analysis,” IRBM, vol. 43, no. 3, pp. 198-209, 2021. 1152 The International Arab Journal of Information Technology, Vol. 22, No. 6, November 2025 https://doi.org/10.1016/j.irbm.2021.01.002

[27] Mebarkia K. and Reffad A., “Multi Optimized SVM Classifiers for Motor Imagery Left and Right Hand Movement Identification,” Australasian Physical and Engineering Sciences in Medicine, vol. 42, pp. 949-958, 2019. https://doi.org/10.1007/s13246-019-00793-y

[28] Meng J., Zhang S., Bekyo A., Olsoe J., and et al., “Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks,” Scientific Reports, vol. 6, pp. 1-15, 2016. https://doi.org/10.1038/srep38565

[29] Mishuhina V. and Jiang X., “Feature Weighting and Regularization of Common Spatial Patterns in EEG-based Motor Imagery BCI,” IEEE Signal Processing Letters, vol. 25, no. 6, pp. 783-787, 2018. https://doi.org/10.1109/LSP.2018.2823683

[30] Mohammadi E., Daneshmand P., and Khorzooghi S., “Electroencephalography-based Brain- Computer Interface Motor Imagery Classification,” Journal of Medical Signals and Sensors, vol. 12, no. 1, pp. 40-47, 2021. https://doi.org/10.4103/jmss.JMSS_74_20

[31] Ocak H., “Optimal Classification of Epileptic Seizures in EEG Using Wavelet Analysis and Genetic Algorithm,” Signal Processing, vol. 88, no. 7, pp. 1858-1867, 2008. https://doi.org/10.1016/j.sigpro.2008.01.026

[32] Pfurtscheller G. and Neuper C., “Motor Imagery and Direct Brain-Computer Communication,” Proceedings of the IEEE, vol. 89, no. 7, pp. 1123- 1134, 2001. https://doi.org/10.1109/5.939829

[33] Pfurtscheller G., Neuper C., Schlogl A., and Lugger K., “Separability of EEG Signals Recorded During Right and Left Motor Imagery Using Adaptive Autoregressive Parameters,” IEEE Transactions on Rehabilitation Engineering, vol. 6, no. 3, pp. 316-325, 1998. https://doi.org/10.1109/86.712230

[34] Pinheiro O., Alves L., and Souza J., “EEG Signals Classification: Motor Imagery for Driving an Intelligent Wheelchair,” IEEE Latin America Transactions, vol. 16, no. 1, pp. 254-259, 2018. https://doi.org/10.1109/TLA.2018.8291481

[35] Reddy V. and Ravi Kumar A., “Multi-Channel Neuro Signal Classification Using Adam-based Coyote Optimization Enabled Deep Belief Network,” Biomedical Signal Processing and Control, vol. 77, pp. 103774, 2022. https://doi.org/10.1016/j.bspc.2022.103774

[36] Reffad A. and Mebarkia K., “Motor Imagery hand Movements Recognition Using SVM Classifier and Genetic Algorithm Optimization,” in Proceedings of the 19th International Multi- Conference on Systems, Signals and Devices, Setif, pp. 1125-1129, 2022. https://doi.org/10.1109/SSD54932.2022.9955863

[37] Rossi E., Pereira Soares S., Prystauka Y., Nakamura M., and Rothman J., “Riding the (Brain) Waves! Using Neural Oscillations to Inform Bilingualism Research,” Bilingualism: Language and Cognition, vol. 26, no. 1, pp. 202-215, 2023. https://doi.org/10.1017/S1366728922000451

[38] Salimpour S., Kalbkhani H., Seyyedi S., and Solouk V., “Stockwell Transform and Semi- Supervised Feature Selection from Deep Features for Classification of BCI Signals,” Scientific Reports, vol. 12, pp. 1-19, 2022. https://doi.org/10.1038/s41598-022-15813-3

[39] Sidaoui B. and Sadouni K., “Epilepsy Seizure Prediction from EEG Signal Using Machine Learning Techniques,” Advances in Electrical and Computer Engineering, vol. 23, no. 2, pp. 47-54, 2023. https://doi.org/10.4316/AECE.2023.02006

[40] Smrdel A., “Use of Common Spatial Patterns for Early Detection of Parkinson’s Disease,” Scientific Reports, vol. 12, pp. 1-10, 2022. https://doi.org/10.1038/s41598-022-23247-0

[41] Tabar Y. and Halici U., “A Novel Deep Learning Approach for Classification of EEG Motor Imagery Signals,” Journal of Neural Engineering, vol. 14, no. 1, pp. 016003, 2017. https://doi.org/10.1088/1741-2560/14/1/016003

[42] Tibrewal N., Leeuwis N., and Alimardani M., “Classification of Motor Imagery EEG Using Deep Learning Increases Performance in Inefficient BCI Users,” PLoS ONE, vol. 17, no. 7, pp. 1-18, 2022. https://doi.org/10.1371/journal.pone.0268880

[43] Too J., Abdullah A., and Saad N., “Classification of Hand Movements Based on Discrete Wavelet Transform and Enhanced Feature Extraction,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 6, pp. 83-89, 2019. https://doi.org/10.14569/IJACSA.2019.0100612

[44] Wan Ismail W., Hanif M., and Hamzah N., “Human Emotion Detection via Brain Waves Study by Using Electroencephalogram (EEG),” International Journal of Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1005-1011, 2016. https://ijaseit.insightsociety.org/index.php/ijaseit/ article/view/1072/pdf_297

[45] You Y., Chen W., and Zhang T., “Motor Imagery EEG Classification Based on Flexible Analytic Wavelet Transform,” Biomedical Signal Processing and Control, vol. 62, pp. 102069, 2020. https://doi.org/10.1016/j.bspc.2020.102069

[46] Zhang W., Huang R., and Ye L., “Evaluation of Emission Reduction Performance of Power Enterprises Based on Least Squares Support Vector Machine,” The International Arab Journal of Information Technology, vol. 21, no. 5, pp. 854- 865, 2024. https://doi.org/10.34028/iajit/21/5/7

[47] Zhong M., Lotte F., Girolami M., and Lecuyer A., Enhancing Motor Imagery EEG Classification Accuracy Using Weight Features Function 1153 “Classifying EEG for Brain Computer Interfaces Using Gaussian Processes,” Pattern Recognition Letters, vol. 29, no. 3, pp. 354-359, 2008. https://doi.org/10.1016/j.patrec.2007.10.009