  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.
