Happy or sad? Recognizing emotions with wavelet coefficient energy mean of EEG signals

Abstract

BACKGROUND: Emotional intelligence plays a vital role in human-computer interaction, and EEG signals are an objective response to human emotions. OBJECTIVE: We propose a method to extract the energy means of detail coefficients as feature values for emotion recognition helps to improve EEG signal-based emotion recognition accuracy. METHOD: We used movie clips as the eliciting material to stimulate the real emotions of the subjects, preprocessed the collected EEG signals, extracted the feature values, and classified the emotions based on them using Support Vector Machine (SVM) and Stacked Auto-Encoder (SAE). The method was verified based on the SJTU emotion EEG database (SEED) and the self-acquisition experiment. RESULTS: The results show that the accuracy is better using SVM. The results based on the SEED database are 89.06% and 79.90% for positive-negative and positive-neutral-negative, respectively. The results based on the self-acquisition data are 98.05% and 89.83% for the same, with an average recognition rate of 86.57% for the four categories of fear, sad (negative), peace (neutral) and happy (positive). CONCLUSION: The results demonstrate the validity of the feature values and provide a theoretical basis for implementing human-computer interaction.