Classification of Facial Expressions from EEG signals using Wavelet Packet Transform and SVM for Wheelchair Control Operations

Abstract

This paper describes the classification of facial expressions using EEG data. The entire procedure aims at controlling an electric wheelchair with a Brain Computer Interface (BCI) headset. The goal is to help the people who are suffering from locked-in syndrome to move or to pass the necessary signals. The headset consists of the electroencephalogram (EEG) cap comprising 16 electrodes attached to the amplifier out of which 14 electrodes are used for data acquisition while two are used as reference and ground. The EEG cap is placed on the head of the subject and various expressions (blink, eyebrows raise, smile, etc.) are performed on the subject. Muscle activities due to facial expressions can be observed from the recorded EEG signals. Expressions are classified and necessary signals are generated. The features are extracted using the wavelet packet transform processing method and classified primarily using Support Vector Machine (SVM).