Real-Time Electroencephalography-Based Emotion Recognition System

This paper proposes parametric, general and effectively automatic real time classification method of electroencephalography (EEG) signals based on emotions. The specific characteristics of the high-frequency signals (alpha, beta, gamma) are observed, and then Fourier Transform, Features Extraction (mean, standard deviation, power) and the K-Nearest Neighbors (KNN) are employed for signal processing, analysis and classification. The proposed method consists of two stages for a multi-class classification and it can be considered as the framework of multi-emotions based on Brain Computer Interface (BCI). The first stage, the calibration, is off-line and it computes the signal processing, determines the features and trains the classification. The second stage, the real-time, is the test on new data. The FFT is applied to avoid redundancy in the selected features; then the classification is carried out using the KNN. The results show that the average accuracy results are 82.33% (valence) and 87.32% (arousal).