运动想象脑电分类中可解释性与性能的平衡：ANFIS-FBCSP-PSO与EEGNet的对比研究 (Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet)

Achieving both accurate and interpretable classification of motor imagery EEG remains a key challenge in brain computer interface (BCI) research. This paper compares a transparent fuzzy reasoning approach (ANFIS-FBCSP-PSO) with a deep learning benchmark (EEGNet) using the 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 percent +/- 13.76 percent accuracy, kappa = 58.04 percent +/- 18.43), while in cross-subject (LOSO) tests, the deep model exhibited stronger generalization (68.20 percent +/- 12.13 percent accuracy, kappa = 57.33 percent +/- 16.22). The study provides practical guidance for selecting MI-BCI systems according to design goals: interpretability or robustness across users. Future investigations into transformer based and hybrid neuro symbolic frameworks are expected to advance transparent EEG decoding.

翻译：在脑机接口研究中，实现运动想象脑电信号既准确又可解释的分类仍是一个关键挑战。本文使用BCI Competition IV-2a数据集，对比了透明模糊推理方法（ANFIS-FBCSP-PSO）与深度学习基准模型（EEGNet）。ANFIS流程结合了滤波器组共空间模式特征提取与通过粒子群优化优化的模糊IF-THEN规则，而EEGNet直接从原始脑电数据中学习层次化的时空表示。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）。本研究为根据设计目标（可解释性或跨用户鲁棒性）选择运动想象脑机接口系统提供了实用指导。未来对基于Transformer的混合神经符号框架的研究有望推动透明脑电解码的发展。