Emotional recognition of EEG signals based on fractal dimension

Abstract

A method based on fractal dimension is proposed to identify EEG emotional signals, and fractal dimension is introduced as an eigenvalue into emotion recognition research. The design experiment obtains the EEG raw data of the experimenter and uses the experimental video to locate and capture the effective signal from the original data. After the pre-electron interference and low-pass filtering are applied, the effective signal is subjected to principal component analysis. The dimension reduction dimension is obtained by reducing the dimension. The support vector machine (SVM) and K nearest neighbor (KNN) classification algorithm are used to classify the eigenvalues to obtain their respective accuracy. The results show that the EEG emotion recognition method based on fractal dimension can distinguish different emotions, and the highest accuracy rate is 83.33%. Therefore, fractal dimension is feasible as the characteristic value of emotion recognition.