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.

我们介绍了一种改进的增量学习算法，用于进化颗粒神经网络分类器（eGNN-C+）。我们使用双边界超立方体来表示颗粒，并自定义适应过程以增强外部边界对数据覆盖和噪声抑制的鲁棒性，同时确保内部边界保持灵活以捕捉漂移。分类器从头开始进化，即时融入新的类别，并进行局部增量特征加权。作为应用，我们专注于对脑电图（EEG）信号中与情绪相关的模式的分类。情绪识别对于增强计算机系统的逼真性和交互性至关重要。我们从 28 个参与玩电脑游戏的个体获得的 EEG 信号的傅立叶频谱中提取特征 - 这是一个公共数据集。每个游戏引发不同的主导情绪：无聊、平静、恐惧或喜悦。我们分析个体电极、时间窗口长度和频带，以评估用户无关的神经模型的准确性和可解释性。研究结果表明，两个大脑半球都有助于分类，尤其是颞叶（T8）和顶叶（P7）区域的电极，并且前额叶和枕叶电极也对分类有贡献。虽然模式可能表现在任何频带上，但阿尔法波（8-13Hz）、δ（1-4Hz）波和 θ（4-8Hz）波以此顺序与情绪类别有更高的相关性。eGNN-C + 在学习 EEG 数据方面表现出有效性。即使在高度随机的时间变化的 4 类分类问题面前，它在 10 秒时间窗口内达到了 81.7% 的准确性和 0.0029 II 的可解释性。