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Spectral Features of EEG Signals for the Automated Recognition of Negative Emotional States

In the presented paper we investigate the properties of spectral EEG features for the detection of negative emotional states. In particular, the proposed features represent the dynamics of energy distribution in the frequency range of 20–35 Hz, based on a time-frequency analysis of multichannel EEG signal. The experimental evaluation is based on data from the DEAP database. We report results with J48- and SMO-based classifiers, in terms of average classification accuracy, 94.3% and 96.8%, respectively.