Sezgin, NecmettinErtugrul, Omer FarukTekin, RamazanTagluk, Mehmet Emin2024-08-042024-08-042017978-1-5386-1880-6https://hdl.handle.net/11616/981012017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEYIn this study, two-channel surface electromyogram (sEMG) signals were used to classify hand open/close with fingers. The bispectrum analysis of the sEMG signal recorded with surface electrodes near the region of the muscle bundles on the front and back of the forearm was classified by extreme learning machines (ELM) based on phase matches in the EMG signal. EMG signals belonging to 17 persons, 8 males and 9 females, with an average age of 24 were used in the study. The fingers were classified using ELM algorithm with 94.60% accuracy in average. From the information obtained through this study, it seems possible to control finger movements and hand opening/closing by using muscle activities of the forearm which we hope to lead to control of intelligent prosthesis hands with high degree of freedom.trinfo:eu-repo/semantics/closedAccessSurface electromyogramExtreme learning machineBispectrum analysisConference Object2-s2.0-85039924448N/AWOS:000426868700008N/A