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

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).