TY - GEN
T1 - Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games
AU - Leite, Daniel
AU - Frigeri, Volnei
AU - Medeiros, Rodrigo
N1 - Funding Information:
This work received support from the Serrapilheira Institute (1812-26777) and from the National Council for Scientific and
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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, i.e., algorithms that customize models to users with no subject-specific calibration data. We describe an evolving Gaussian Fuzzy Classifier (eGFC), which is supported by a 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 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 more correlated with the 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.
AB - 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, i.e., algorithms that customize models to users with no subject-specific calibration data. We describe an evolving Gaussian Fuzzy Classifier (eGFC), which is supported by a 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 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 more correlated with the 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.
UR - http://www.scopus.com/inward/record.url?scp=85123819603&partnerID=8YFLogxK
U2 - 10.1109/LA-CCI48322.2021.9769842
DO - 10.1109/LA-CCI48322.2021.9769842
M3 - Conference contribution
AN - SCOPUS:85123819603
T3 - 2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
BT - 2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
Y2 - 2 November 2021 through 4 November 2021
ER -