EEG Based Emotion Recognition Using Higher Order Crossings

Abstract

Human emotions govern our quality of relations within the society. It can be expressed as a state of mind which predicts the response of a person whether positive or negative to a particular situation. It is a versatile phenomenon which controls the reactions and behaviour of the person depending upon his mental state whether he is happy, angry, frustrated, sad, excited and scared. Emotions can be predicted from gestures, sound processing and facial expression can be faked but emotion recognition using EEG signals is very powerful method to know the internal state of mind accurately. In this dissertation, the aim of the study is to design an interactive and a smart two class system for emotion recognition based on Electroencephalogram (EEG) signals. In this study EEG signal is acquired on frontal electrodes such as F1, F2, F3, F4 and FZ, central electrode Cz and parietal electrode Pz from eight subjects for classification of emotions into two classes namely HVLA (High Valence Low Arousal) and LVHA (Low Valence High Arousal). The images provided by International Affective Picture System (IAPS) have been used for evoking emotions. The data has been acquired in two ways, one by placing an EEG cap on the head of a subject as per 10-20 International system, using BIOPAC data acquisition unit. The obtained EEG signal has been filtered in offline mode (ACQ 4.2 software) by using low pass IIR filter, high pass IIR filter and a notch filter. The low pass IIR filter is followed by high pass IIR filter. IIR filters have been used to bring the EEG signal in the frequency range of 0.5 to 40Hz. The notch filter has been used to remove the 50Hz power noise interference and second by acquiring EEG signal from the data available on the enterface 06 website. The analysis has been performed using frontal electrodes F1, F2, F3, F4, and Fz, central electrode Cz and parietal electrode Pz. After EEG data acquisition, the acquired EEG signal is signal conditioned using the EDF Browser. Firstly, a Butterworth Band pass filter of 0.5 Hz to 40 Hz has been applied on the EEG Signals to bring the signal in the desired range, followed by a Resonator Notch filter with notch frequency of 50 Hz, to remove the power noise interference in the signal. Then averaging of EEG signal for each of the seven channels F1, F2, F3, F4, Fz, Cz and Pz is performed on which an IIR Butterworth Bandpass filter is applied to keep only alpha band (8-12 Hz) frequencies and beta band (13–30 Hz) frequencies. Then features called Higher Order Crossings i.e. zero crossing counts are extracted for Emotion Classification. The classification has been performed using two classifiers – Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) with 10 fold cross validation to classify emotions into two classes.