EEG-Based Emotion Recognition Using Principal Component Analysis and Support Vector Machine

Recognition of human's emotional states is one of crucial issues in many Brain-Computer-Interface (BCI) applications. In the current study, the problem of emotion recognition based on EEG data will be addressed. The public SEED-V EEG dataset acquired during affect elicitation by audio-visual stimuli was first presented. Then, the preprocessing techniques including noise filtering, down sampling, trail separation were employed on the raw data. To reduce the size of data as well as remove less valuable data, the Principal Component Analysis (PCA) was proposed. Next, the Power Spectral Density (PSD) method was used to extract emotional features. Finally, we utilized the Support Vector Machine (SVM) for emotion classification. The obtained results show that a high classification accuracy (up to 95.6%) can be achieved. In addition, the obtained results also demonstrate that the proposed PCA technique greatly contributes to improving classification accuracy.