Emotion Recognition Using EEG-Based Brain Computer Interface

EEG signals can assess human emotions that perform significantly in promoting robust brain-computer interface systems. This study has been conducted utilizing a public emotional EEG dataset called SEED. Before performing classification from the EEG signals, features have been extracted to acquire information. DWT has been introduced to disintegrate the preprocessed EEG signals into five separate frequency sub-bands (alpha, beta, gamma, delta, and theta), and the statistical features of these DWT coefficients have been computed in the frequency domain. We applied three different wavelet functions to extract multiple features from the preprocessed signals and considered three statistical ones to distinguish relevant information about EEG signals. The wavelet functions are "coif5", "db4", and "db8", and the statistical features are entropy, power, and standard deviation. In this experiment, the efficiency of emotion identification has been investigated for a set of 62 EEG channels. A non-linear SVM classifier has been trained using the features to categorize the signals into three mental states: negative, neutral and positive by applying 5-fold cross-validation. In our model, we achieved a training accuracy of 70% 83.75% and 82% on 62 channels for positive, neutral and negative, respectively.