Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games

• URL: http://arxiv.org/abs/2103.03488v1
• Date: Fri, 5 Mar 2021 06:27:04 GMT
• Title: Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games
• Authors: Daniel Leite, Volnei Frigeri Jr., Rodrigo Medeiros
• Abstract summary: Fuzzy eGFC is supported by an online semi-supervised learning algorithm to recognize emotion patterns. We analyze the effect of individual electrodes, time window lengths, and frequency bands on the accuracy of userindependent eGs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Human emotion recognition has become a need for more realistic and interactive machines and computer systems. The greatest challenge is the availability of high-performance algorithms to effectively manage individual differences and nonstationarities in physiological data streams, i.e., algorithms that self-customize to a user with no subject-specific calibration data. We describe an evolving Gaussian Fuzzy Classifier (eGFC), which is supported by an online semi-supervised learning algorithm to recognize emotion patterns from electroencephalogram (EEG) data streams. We extract features from the Fourier spectrum of EEG data. The data are provided by 28 individuals playing the games 'Train Sim World', 'Unravel', 'Slender The Arrival', and 'Goat Simulator' - a public dataset. Different emotions prevail, namely, boredom, calmness, horror and joy. We analyze the effect of individual electrodes, time window lengths, and frequency bands on the accuracy of user-independent eGFCs. We conclude that both brain hemispheres may assist classification, especially electrodes on the frontal (Af3-Af4), occipital (O1-O2), and temporal (T7-T8) areas. We observe that patterns may be eventually found in any frequency band; however, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz) bands, in this order, are the highest correlated with emotion classes. eGFC has shown to be effective for real-time learning of EEG data. It reaches a 72.2% accuracy using a variable rule base, 10-second windows, and 1.8ms/sample processing time in a highly-stochastic time-varying 4-class classification problem.

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