Evaluation of Cepstral Coefficients as Features in EEG-Based Recognition of Emotional States
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The study of physiological signals and the evaluation of their features are of great importance for the automated emotion detection, as these are directly connected with the successful modelling and classification of the states of interest. In the presented work, we present an evaluation of the appropriateness of LFCC and the logarithmic energy of signals as features for automated recognition of negative emotional states in terms of recognition accuracy. In particular, three sets of features are compared – features computed after frame-level segmentation of the signal; features computed after averaging of frame level descriptors; and features extracted from an entire EEG recording. The performance of the extracted features is evaluated using C4.5 classifier for 10, 15, 20, 30, 45, and 60 filters.
KeywordsLFCC features Cepstral coefficients Emotion recognition EEG
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