Emotion detection from EEG recordings based on supervised and unsupervised dimension reduction

In recent years, researchers have been trying to detect human emotions from recorded brain signals such as electroencephalogram (EEG) signals. However, due to the high levels of noise from the EEG recordings, a single feature alone cannot achieve good performance. A combination of distinct features is the key for automatic emotion detection. In this paper, we present a hybrid dimension feature reduction scheme using a total of 14 different features extracted from EEG recordings. The scheme combines these distinct features in the feature space using both supervised and unsupervised feature selection processes. Maximum Relevance Minimum Redundancy (mRMR) is applied to re-order the combined features into max-relevance with the labels and min-redundancy of each feature. The generated features are further reduced with principal component analysis (PCA) for extracting the principal components. Experimental results show that the proposed work outperforms the state-of-art methods using the same settings in the publicly available DEAP data set.