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.

翻译：我们提出了一种改进的增量学习算法，用于演化粒度神经网络分类器（eGNN-C+）。我们采用双边界超立方体表示粒度，并定制适应性过程以增强外部的鲁棒性，用于数据覆盖和噪声抑制，同时确保内部保持灵活性以捕获数据漂移。该分类器从零开始演化，可即时整合新类别，并进行局部增量特征加权。作为应用，我们聚焦于脑电图（EEG）信号中情绪相关模式的分类。情绪识别对于提升计算机系统的真实感和交互性至关重要。我们从28名参与电脑游戏（公开数据集）个体的脑电信号傅里叶频谱中提取特征，每种游戏引发不同主导情绪：无聊、平静、恐惧或喜悦。我们分析单个电极、时间窗口长度及频带，以评估最终用户无关神经模型的准确性和可解释性。结果表明，双侧脑半球均有助于分类，尤其是颞叶（T8）和顶叶（P7）区域的电极，同时额叶和枕叶电极也起到作用。尽管模式可能出现在任何频带，但Alpha（8-13Hz）、Delta（1-4Hz）和Theta（4-8Hz）频带（按此顺序）与情绪类别具有更高相关性。eGNN-C+在学习脑电数据方面表现出有效性。面对高度随机时变的四类分类问题，使用10秒时间窗口时，其准确率达到81.7%，可解释性指标II为0.0029。