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)