Effect of Decision Way on the EEG Signal Classification Performance of Motor Imagery

Timely and accurate classification of motor imagery (MI) is crucial for its practical application. However, the effect of decision way has not been thoroughly researched in the existing studies. This paper researches the effect of decision way on the classification of MI task electroencephalogram (EEG) signals. The time-frequency analysis of pre-processed EEG signals is carried out to obtain the variation rules of different EEG rhythms in MI tasks. The power changes of MI task EEG signals are analyzed. The time domain and frequency domain features of EEG signals are extracted, and the MI task classification models with different decision ways are built. The accuracy of different decision ways on the classification of EEG signals in MI tasks are compared.