en 4-Biomedical Signal Processing Biomedical Signal Processing Research Paper Research Paper <div style="text-align: justify;"><span style="color:#000000;"><span style="font-size:14px;"><span style="font-family:times new roman;">In this study a method has been introduced to map the features extracted from the recorded electromyogram signals from the forearm and the force generated by the fingers. In order to simultaneously record of sEMG signals and the force produced by fingers, 9 requested movements of fingers conducted by 10 healthy people. Estimation was done for 6 degrees of freedom (DoF) and generalized regression neural network (GRNN) was selected for system training. The optimal parameters, including the length of the time windows, the parameters of the neural network, and the characteristics of the sEMG signal were calculated to improve the performance of the estimate. The performance was obtained based on R² criterion. The Total value of R² for 6 DoF was 92.8&plusmn;5.2% that obtained by greedy looking system parameters in all the subjects. The result shows that proposed method can be significant in simultaneous myoelectric control.</span></span></span></div> Surface Electromyogram Signals (sEMG),Generalized Neural Network (GRNN),R2 Criterion, 308 313 http://ijeee.iust.ac.ir/browse.php?a_code=A-10-194-2&slc_lang=en&sid=1 S. Mirzakuchaki m_kuchaki@iust.ac.ir 180031947532846007023 180031947532846007023 Yes Department of Electrical and Electronic Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran, Iran. Z. Paydar zohreh_paydar@elec.iust.ac.ir 180031947532846007024 180031947532846007024 No Department of Electrical and Electronic Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran, Iran.