{"ID":2847161,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00369","arxiv_id":"2511.00369","title":"Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet","abstract":"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.","short_abstract":"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 Competi...","url_abs":"https://arxiv.org/abs/2511.00369","url_pdf":"https://arxiv.org/pdf/2511.00369v2","authors":"[\"Farjana Aktar\",\"Mohd Ruhul Ameen\",\"Akif Islam\",\"Md Ekramul Hamid\"]","published":"2025-11-01T02:42:01Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.NE\"]","methods":"[\"Transformer\"]","has_code":false}