脑机接口 / BCI

Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet

在运动想象EEG分类中平衡可解释性和性能：ANFIS-FBCSP-PSO和EEGNet的比较研究

Farjana Aktar, Mohd Ruhul Ameen, Akif Islam, Md Ekramul Hamid

机构 * University of Rajshahi（拉贾沙希大学） 纠错

专题命中 运动想象 ：EEG(title,title_cn)；BCI(summary_cn,abstract)；motor imagery(title)；brain-computer interface(abstract)

AI总结 本文比较了ANFIS-FBCSP-PSO与EEGNet在BCI竞赛IV-2a数据集上的性能，发现模糊神经模型在内子试验中表现更优，而深度模型在跨受试者测试中更具泛化能力，为选择MI-BCI系统提供指导。

Comments Accepted at the 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence and Networking (QPAIN 2026)

Journal ref 2026 IEEE 2nd International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN)