Pooya Chanu, MaibamPei, DingyiOlikkal, Parthan SathishkumarVinjamuri, RamanaKakoty, Nayan M.2025-04-232025-04-232023-11-02Pooya Chanu, Maibam, Dingyi Pei, Parthan Olikkal, Ramana Kumar Vinjamuri, and Nayan M Kakoty. “Electroencephalogram Based Control of Prosthetic Hand Using Optimizable Support Vector Machine.” Proceedings of the 2023 6th International Conference on Advances in Robotics, AIR ’23, November 2, 2023, 1–6. https://doi.org/10.1145/3610419.3610453.https://doi.org/10.1145/3610419.3610453http://hdl.handle.net/11603/38048AIR '23: Proceedings of the 2023 6th International Conference on Advances in Robotics, Ropar India, July 5 - 8, 2023Research on electromyogram (EMG) controlled prosthetic hands has advanced significantly, enriching the social and professional lives of people with hand amputation. Even so, the non-functionality of motor neurons in the remnant muscles impedes the generation of EMG as a control signal. However, such people have the same ability as healthy individuals to generate motor cortical activity. The work presented in this paper investigates electroencephalogram (EEG)-based control of a prosthetic hand. EEG of 10 healthy subjects performing the grasping operations were acquired for classification of hand movements. 15 EEG channels were selected to classify hand open and close operations. Hand movement-class-specific time-domain features were extracted from the filtered EEG. A support vector machine (SVM) was employed with 24-fold cross-validation for classification using extracted features. SVM hyper-parameters for the classification model were optimized with a Bayesian optimizer with a minimum prediction error as an objective function. During training and testing of the classifier model, an average accuracy of 96.8 ± 0.98% and 93.4 ± 1.16% respectively, were achieved across the subjects. The trained classifier model was employed to control prosthetic hand open and close operations. This study demonstrates that EEG can be used to control a prosthetic hand by amputees with motor neuron disabilities.6 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Electroencephalogram based Control of Prosthetic Hand using Optimizable Support Vector MachineText