Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques

Keywords: Upper limb amputees, Prosthetic control, EEG and EMG signals, Machine learning, Movement recognition


Amputation of the upper limb significantly hinders the ability of patients to perform activities of daily living. To address this challenge, this paper introduces a novel approach that combines non-invasive methods, specifically Electroencephalography (EEG) and Electromyography (EMG) signals, with advanced machine learning techniques to recognize upper limb movements. The objective is to improve the control and functionality of prosthetic upper limbs through effective pattern recognition. The proposed methodology involves the fusion of EMG and EEG signals, which are processed using time-frequency domain feature extraction techniques. This enables the classification of seven distinct hand and wrist movements. The experiments conducted in this study utilized the Binary Grey Wolf Optimization (BGWO) algorithm to select optimal features for the proposed classification model. The results demonstrate promising outcomes, with an average classification accuracy of 93.6% for three amputees and five individuals with intact limbs. The accuracy achieved in classifying the seven types of hand and wrist movements further validates the effectiveness of the proposed approach. By offering a non-invasive and reliable means of recognizing upper limb movements, this research represents a significant step forward in biotechnical engineering for upper limb amputees. The findings hold considerable potential for enhancing the control and usability of prosthetic devices, ultimately contributing to the overall quality of life for individuals with upper limb amputations.


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Author Biographies

Huda M. Radha, Department of Computer Science, College of Science, University of Baghdad, Baghdad, Iraq

Huda M. Ridha is a Lecturer at the Department of Computer Science, College of Science, University of Baghdad. She got a B.Sc. and M.Sc. degrees in Computer Science. Her research interests are in Artificial Intelligent, Image Processing, Data Mining and Bio-signals analysis.

Alia K. Abdul Hassan, Department of Computer Science, University of Technology, Baghdad, Iraq

Alia K. Abdul Hassan is a Professor at the Department of Computer Science, University of  Technology, Baghdad, Iraq. She got her B.Sc. degree in Computer Science, the M.Sc. degree in Computer Science and the Ph.D. degree in Computer Science. Her research interests are in Artificial Intelligent, Soft computing, Data Mining, Software engineering, Electronic Management, and Computer security.

Ali H. Al-Timemy, Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Iraq

Ali H. Al-Timemy is a Assistant Prof. at the Department of Biomedical Engineering, College of Al-Khwarizmi of Engineering, University of Baghdad, Iraq. He got his B.Sc. degree in Biomedical Engineering, the M.Sc. degree in Biomedical Engineering and Ph.D. degree in Biomedical Engineering] from the Centre for Robotics and Neural Systems (CRNS), Plymouth University, UK. . is research interests are in Bio-medical signal, Image processing, Ppattern recognition, Artificial intelligence and Machine Learning.


Ahakonye, L.A.C., Nwakanma, C.I., Lee, J.M., and Kim, D.S., 2023. SCADA intrusion detection scheme exploiting the fusion of modified decision tree and Chi-square feature selection. Internet of Things, 21, p.100676. DOI: https://doi.org/10.1016/j.iot.2022.100676

Alelyani, S., Tang, J., and Liu, H., 2018. Feature selection for clustering: A review. In: Data Clustering. CRC Press, United States, p.29-60. Avaialable from: https://www.taylorfrancis.com/chapters/edit/10.1201/9781315373515-2/feature-selection-clustering-review-salem-alelyani-jiliang-tang-huan-liu [Last accessed on 2023 Jul 01]. DOI: https://doi.org/10.1201/9781315373515-2

Al-Quraishi, M.S., Elamvazuthi, I., Tang, T.B., Al-Qurishi, M., Parasuraman, S., and Borboni, A., 2021. Multimodal fusion approach based on EEG and EMG signals for lower limb movement recognition. IEEE Sensors Journal, 21(24), p.27640-27650. DOI: https://doi.org/10.1109/JSEN.2021.3119074

Alzubi, J., Nayyar, A., and Kumar, A., 2018. Machine learning from theory to algorithms: An overview. Journal of physics: Conference series, 1142, p.012012. DOI: https://doi.org/10.1088/1742-6596/1142/1/012012

Beheshti, Z., 2022. BMPA-TVSinV: A binary marine predators algorithm using time-varying sine and V-shaped transfer functions for wrapper-based feature selection. Knowledge-Based Systems, 252, p.109446 DOI: https://doi.org/10.1016/j.knosys.2022.109446

Cai, H., Qu, Z., Li, Z., Zhang, Y., Hu, X., and Hu, B., 2020. Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion, 59, pp.127-138. DOI: https://doi.org/10.1016/j.inffus.2020.01.008

Dey, N., Borra, S., Ashour, A., and Shi, F., 2018. Machine Learning in Bio-Signal Analysis and Diagnostic Imaging. Academic Press, United States. Available from: https://books.google.iq/books?id=3OR8DwAAQBAJ&dq=Machine+learning+in+bio-signal+analysis+and+diagnostic+imaging:+academic+press.&lr=&hl=ar&source=gbs_navlinks_s [Last accessed on 2023 Jul 01].

Fang, C., He, B., Wang, Y., Cao, J., and Gao, S., 2020. EMG-centered multisensory based technologies for pattern recognition in rehabilitation: State of the art and challenges. Biosensors (Basel), 10(8), p.85. DOI: https://doi.org/10.3390/bios10080085

García, S., Luengo, J., and Herrera, F., 2015. Data Preprocessing in Data Mining.Vol. 72. Springer, Germany. DOI: https://doi.org/10.1007/978-3-319-10247-4

Hooda, N., Das, R., and Kumar, N., 2020. Fusion of EEG and EMG signals for classification of unilateral foot movements. Biomedical Signal Processing and Control, 60, p.101990. DOI: https://doi.org/10.1016/j.bspc.2020.101990

Jović, A., Brkić, K., and Bogunović, N., 2015. A review of feature selection methods with applications. In: Paper Presented at the 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). DOI: https://doi.org/10.1109/MIPRO.2015.7160458

Khan, S.M., Khan, A.A., and Farooq, O., 2019. Selection of features and classifiers for EMG-EEG-based upper limb assistive devices-a review. IEEE Reviews in Biomedical Engineering, 13, pp.248-260. DOI: https://doi.org/10.1109/RBME.2019.2950897

Krishnan, S., and Athavale, Y., 2018. Trends in biomedical signal feature extraction. Biomedical Signal Processing and Control, 43, pp.41-63. DOI: https://doi.org/10.1016/j.bspc.2018.02.008

Kumar, R., Singh, A.K., and Mukaherjee, S., 2021. Review of An EMG-Controlled Prosthetic Arm. In: Computational Methodologies for Electrical and Electronics Engineers. IGI Global, United States. pp. 85-92. DOI: https://doi.org/10.4018/978-1-7998-3327-7.ch007

Nanga, S., Bawah, A.T., Acquaye, B.A., Billa, M.I., Baeta, F.D., Odai, N.A., and Nsiah, A.D., 2021. Review of dimension reduction methods. Journal of Data Analysis and Information Processing, 9(3), pp.189-231. DOI: https://doi.org/10.4236/jdaip.2021.93013

Radha, H.M., Abdul Hassan, A.K., and Al-Timemy, A.H., 2023. Enhanced prosthesis control through improved shoulder girdle motion recognition using time-dependent power spectrum descriptors and long short-term memory. Mathematical Modelling of Engineering Problems, 10(3), pp.861-870. DOI: https://doi.org/10.18280/mmep.100316

Radha, H.M., Abdul Hassan, A.K., and H Al-Timemy, A., 2022. Classification of different shoulder girdle motions for prosthesis control using a time-domain feature extraction technique. Aro-the Scientific Journal of Koya University, 10(2), pp.73-81. DOI: https://doi.org/10.14500/aro.11064

Shahana, A., and Preeja, V., 2016, Survey on feature subset selection for high dimensional data. In: Paper Presented at the 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). DOI: https://doi.org/10.1109/ICCPCT.2016.7530147

Singer, G., Anuar, R., and Ben-Gal, I., 2020. A weighted information-gain measure for ordinal classification trees. Expert Systems with Applications, 152, p.113375. DOI: https://doi.org/10.1016/j.eswa.2020.113375

Thabtah, F., Kamalov, F., Hammoud, S., and Shahamiri, S.R., 2020. Least Loss: A simplified filter method for feature selection. Information Sciences, 534, p.1-15. DOI: https://doi.org/10.1016/j.ins.2020.05.017

Udhaya Kumar, S., and Hannah Inbarani, H., 2017. PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task. Neural Computing and Applications, 28, p.3239-3258. DOI: https://doi.org/10.1007/s00521-016-2236-5

Verma, G.K., and Tiwary, U.S., 2014. Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals. Neuroimage, 102, p.162-172. DOI: https://doi.org/10.1016/j.neuroimage.2013.11.007

Voelzke, J., 2015. Weakening the gain-loss-ratio measure to make it stronger. Finance Research Letters, 12, p.58-66. DOI: https://doi.org/10.1016/j.frl.2014.11.007

How to Cite
Radha, H. M., Abdul Hassan, A. K. and Al-Timemy, A. H. (2023) “Enhancing Upper Limb Prosthetic Control in Amputees Using Non-invasive EEG and EMG Signals with Machine Learning Techniques”, ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 11(2), pp. 99-108. doi: 10.14500/aro.11269.