Achieving both accurate and interpretable classification of motor-imagery EEG remains a key challenge in brain-computer interface (BCI) research. In this paper, we compare a transparent fuzzy-reasoning approach (ANFIS-FBCSP-PSO) with a well-known deep-learning benchmark (EEGNet) using the publicly available BCI Competition IV-2a dataset. The ANFIS pipeline combines filter-bank common spatial pattern feature extraction with fuzzy IF-THEN rules optimized via particle-swarm optimization, while EEGNet learns hierarchical spatial-temporal representations directly from raw EEG data. In within-subject experiments, the fuzzy-neural model performed better (68.58% +/- 13.76% accuracy, kappa = 58.04% +/- 18.43), while in cross-subject (LOSO) tests, the deep model exhibited stronger generalization (68.20% +/- 12.13% accuracy, kappa = 57.33% +/- 16.22). The study therefore provides practical guidance for selecting MI-BCI systems according to the design goal: interpretability or robustness across users. Future investigations into transformer-based and hybrid neuro-symbolic frameworks are expected to further advance transparent EEG decoding.

翻译：在脑机接口研究中，如何同时实现运动想象脑电信号的准确分类与模型可解释性仍是一个核心挑战。本文使用公开的BCI Competition IV-2a数据集，比较了基于透明模糊推理的方法（ANFIS-FBCSP-PSO）与经典的深度学习基准模型（EEGNet）。ANFIS框架融合了滤波器组共空间模式特征提取与通过粒子群算法优化的模糊IF-THEN规则，而EEGNet则直接从原始脑电数据中学习层次化的时空表征。在受试者内实验中，模糊神经模型表现更优（准确率68.58% +/- 13.76%，kappa = 58.04% +/- 18.43）；而在跨受试者（留一受试者交叉验证）测试中，深度学习模型展现出更强的泛化能力（准确率68.20% +/- 12.13%，kappa = 57.33% +/- 16.22）。本研究据此为运动想象脑机接口系统的选择提供了实践指导：可根据设计目标——可解释性或跨用户鲁棒性——进行取舍。未来对基于Transformer的架构及混合神经符号框架的探索，有望进一步推动透明脑电解码技术的发展。