Research on EEG Features in Different Emotional States

In order to improve the detection and treatment of neurological diseases effectively, it is a significant means to analysis EEG features. In this study, extrovert and stable persons were selected as the subjects according to the Eysenck Personality Questionnaire. Then set the subjects’ EEG signals in a quiet state with eyes closed as a reference group. Four types of pure music were selected as stimulus materials to induce four different kinds of emotions: pleasure, sadness, irritability, and fear. During the period, evoked EEG signals was acquired. Then, some signal processing methods were used to de-noise for EEG and separate EOG artifacts from EEG signals. Finally, EEG signals’ features in time domain, frequency domain and time-frequency domain were extracted, especially the method which combined Hilbert transform based on EMD with information entropy to calculate EEG signals’ Hilbert spectrum entropy for four emotional states. The results showed that EEG signals’ features in different emotional states changed with gender, brain and mood objectively, all differences mainly reflected in time domain features, frequency domain features and time-frequency domain features. All the results reveal that EEG signals’ variation characteristics in the process of auditory stimulation, and can be an adjustment basis for detection and treatment of neurological diseases.