对比两种脑电解码方法，兼顾准确率与可解释性。
Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet
- 用模糊推理优化特征提取，提升模型可解释性
- 跨被试测试中深度模型准确率达68.20%
- 适合对结果可解释性有要求的研究场景
在脑机接口研究中，实现运动想象脑电信号的高精度与可解释性分类仍是关键挑战。本文比较了一种透明的模糊推理方法（ANFIS-FBCSP-PSO）与经典的深度学习基准模型（EEGNet），使用公开的BCI Competition IV-2a数据集进行评估。ANFIS流程结合滤波器组共空间模式特征提取与粒子群优化的模糊IF-THEN规则，而EEGNet则直接从原始脑电数据中学习分层时空表征。在被试内实验中，模糊神经模型表现更优（准确率68.58% ± 13.76%，κ = 58.04% ± 18.43）；在跨被试（留一被试）测试中，深度模型展现出更强泛化能力（准确率68.20% ± 12.13%，κ = 57.33% ± 16.22）。研究为根据设计目标选择运动想象脑机接口系统提供了实用指导：追求可解释性或跨用户鲁棒性。未来基于Transformer与混合神经符号框架的研究有望进一步推动透明脑电解码发展。
原文摘要 · Abstract (English)
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.
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