Real-time subject-dependent EEG-based emotion recognition algorithm

In this paper, we proposed a real-time subject-dependent EEG-based emotion recognition algorithm and tested it on experiments' databases and the benchmark database DEAP. The algorithm consists of two parts: feature extraction and data classification with Support Vector Machine (SVM). Use of a Fractal Dimension feature in combination with statistical and Higher Order Crossings (HOC) features gave results with the best accuracy and with adequate computational time. The features were calculated from EEG using a sliding window. The proposed algorithm can recognize up to 8 emotions such as happy, surprised, satisfied, protected, angry, frightened, unconcerned, and sad using 4 electrodes in real time. Two experiments with audio and visual stimuli were implemented, and the Emotiv EPOC device was used to collect EEG data