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In recent years, assessment of human emotions from Electroencephalogram (EEG) has one of the active research areas in developing intellectual man-machine interfaces. In this work, we considered two different frequency ranges of EEG signals such as alpha to gamma (8 Hz – 64 Hz) and delta to gamma (0 Hz – 64 Hz) for classifying five emotions (disgust, happy, surprise, fear, and neutral). EEG signals are collected using 64 channels from 20 subjects in the age group of 21~39 years. The raw EEG signals are preprocessed using Surface Laplacian filtering and a set of conventional and proposed statistical features are derived using Discrete Wavelet Transform (DWT). Two linear classifiers (K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA)) are used to map the statistical features into corresponding emotions. One of the proposed features derived by using “db8” wavelet function under alpha to gamma band gives the maximum average classification rate of 86.55% using KNN and 82.70% using LDA compared to delta to gamma band. In addition, the proposed features gives higher classification rate compared to conventional features. Finally we present the average classification accuracy and subsets of discrete emotion classification rates for justifying the performance of our emotion recognition system. The nonverbal communication through emotions, intentions and affective states are the current key areas of research on developing intellectual man-machine systems. Manifestations of emotional states are normally straightforward to detect and understand by humans, as these are reflected in both voice and body languages (Adler and Rodman 2003; Pease and Pease 2004). In recent years, the research efforts in Human Computer Interaction (HCI) are focused on empowering computers to understand human emotions. Most of the efforts have been dedicated to the design of user-friendly and ergonomic systems by means of innovative interfaces such as voice, vision, and gestures. Many literature works have been reported on emotion recognition using facial expressions and speech modalities (Chen and Huang 2000; Daabaj 2002; Hongo et al. 2000; Massaro 2000). These conventional methods of assessing emotions thorough the speech and the facial expressions of a subject are purposefully expressed and it can be more easily concealed by other subjects (Takahashi 2004). Indeed, it cannot be used for those people who have suffered from severe motor disabilities, amyotrophic lateral sclerosis, paralysis, and introverted characters.

Discrete Wavelet Transform Based Selection of Salient EEG Frequency Band for Assessing Human Emotions