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

Daniel Leite, Volnei Frigeri, Rodrigo Medeiros

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728188645
DOI
EstadoPublicada - 2021
Publicado de forma externa
Evento2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021 - Temuco, Chile
Duración: 2 nov. 20214 nov. 2021

Serie de la publicación

Nombre2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021

Conferencia

Conferencia2021 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2021
País/TerritorioChile
CiudadTemuco
Período2/11/214/11/21

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