A method of estimating hand and elbow movements using electrocorticogram(ECoG)signals is proposed.Using multiple channels,surface electromyogram(EMG)signals and ECoG signals were obtained from patients simultaneously.The estimated movements were those to close and then open the hand and those to bend the elbow inward.The patients were encouraged to perform the movements in accordance with their free will instead of after being induced by external stimuli.Surface EMG signals were used to find movement time points,and ECoG signals were used to estimate the movements.To extract the characteristics of the individual movements,the ECoG signals were divided into a total of six bands(the entire band and the δ,θ,α,β and γ bands)to obtain the information entropy,and the maximum likelihood estimation method was used to estimate the movements.The results of the experiment show that the performance averages 74% when the ECoG of γ band is used,which is higher than that when other bands are used,and higher estimation success rates are shown in the γ band than in other bands.The time of the movements is divided into three time sections based on movement time points,and the “Before” section,which includes the readiness potential,is compared with the “Onset” section.In the “Before” section and the “Onset” section,estimation success rates are 66% and 65%,respectively,and thus it is determined that the readiness potential could can be used. A method of estimating hand and elbow movements using electrocorticogram (ECoG) signals is proposed. Using multiple channels, surface electromyogram (EMG) signals and ECoG signals were obtained from patients simultaneously. The estimated movements were those to close and then open the hand and those to bend the elbow inward. The patient were encouraged to perform the movements in accordance with their free will instead of after being induced by external stimuli. Surface EMG signals were used to find movement time points, and ECoG signals were used to estimate the movements. To extract the characteristics of the individual movements, the ECoG signals were divided into a total of six bands (the entire band and the δ, θ, α, β and γ bands) to obtain the information entropy, and the maximum likelihood estimation method was used to estimate the movements. The results of the experiment show that the performance averages 74% when the ECoG of γ band is used, which is higher than that when the other bands are us ed, and higher estimation success rates are shown in the γ band than in other bands. The time of the movements is divided into three time sections based on movement time points, and the “Before” section, which includes the readiness potential, is compared with the “Onset” section. In the “Before” section and the “Onset” section, estimation success rates are 66% and 65% respectively, and so it is determined that the readiness potential could can be used.