  We introduce a modified incremental learning algorithm for evolving Granular
Neural Network Classifiers (eGNN-C+). We use double-boundary hyper-boxes to
represent granules, and customize the adaptation procedures to enhance the
robustness of outer boxes for data coverage and noise suppression, while
ensuring that inner boxes remain flexible to capture drifts. The classifier
evolves from scratch, incorporates new classes on the fly, and performs local
incremental feature weighting. As an application, we focus on the
classification of emotion-related patterns within electroencephalogram (EEG)
signals. Emotion recognition is crucial for enhancing the realism and
interactivity of computer systems. We extract features from the Fourier
spectrum of EEG signals obtained from 28 individuals engaged in playing
computer games -- a public dataset. Each game elicits a different predominant
emotion: boredom, calmness, horror, or joy. We analyze individual electrodes,
time window lengths, and frequency bands to assess the accuracy and
interpretability of resulting user-independent neural models. The findings
indicate that both brain hemispheres assist classification, especially
electrodes on the temporal (T8) and parietal (P7) areas, alongside
contributions from frontal and occipital electrodes. While patterns may
manifest in any band, the Alpha (8-13Hz), Delta (1-4Hz), and Theta (4-8Hz)
bands, in this order, exhibited higher correspondence with the emotion classes.
The eGNN-C+ demonstrates effectiveness in learning EEG data. It achieves an
accuracy of 81.7% and a 0.0029 II interpretability using 10-second time
windows, even in face of a highly-stochastic time-varying 4-class
classification problem.
