计算机应用研究2026，Vol.43Issue(8)：2286-2292,7.DOI:10.19734/j.issn.1001-3695.2025.12.0515
基于内容自适应小波特征与判别式层次原型对齐的跨被试脑电解码网络
Content-adaptive wavelet features and discriminative hierarchical prototype alignment for cross-subject EEG decoding
摘要
Abstract
Cross-subject decoding of motor imagery EEG signals is affected by factors such as non-stationarity and inconsistent feature distributions,leading to insufficient model generalization.To address these issues,this algorithm proposed a cross-subject EEG decoding network(CAWF-DHPA network)based on content-adaptive wavelet features and discriminative hierarchi-cal prototype alignment.The network architecture firstly constructed a content-adaptive wavelet feature extraction module(CAWFE module),which enhanced the stability of spatiotemporal representations by integrating multiscale temporal transient features with relevant frequency-domain information.Subsequently,it employed a hierarchical prototype alignment module(DHPAN module)to achieve collaborative correction of margins and conditional distributions,thereby obtaining feature repre-sentations that possessed both domain invariance and class discriminability.Experiments on the BCI competition Ⅳ 2a and OpenBMI datasets demonstrate that this method achieves average accuracy rates of 79.48％and 77.42％,respectively.The results indicate that the proposed method effectively mitigates inter-subject signal variations,thereby enhancing the stability and generalization potential of MI-EEG decoding in practical deployment.关键词
跨被试/运动想象/脑电信号/深度学习Key words
cross-subject/motor imagery/electroencephalogram/deep learning分类
信息技术与安全科学引用本文复制引用
胡欣欣,曹秒,李晓琳,彭博,周志勇,戴亚康..基于内容自适应小波特征与判别式层次原型对齐的跨被试脑电解码网络[J].计算机应用研究,2026,43(8):2286-2292,7.基金项目
科技部-科技创新项目2030(2022ZD0208502) （2022ZD0208502）
国家自然科学基金资助项目(62471467) （62471467）
中国博士后科学基金面上项目(188005063) （188005063）
苏州市基础研究试点项目(SSD2023008) （SSD2023008）
苏州市重点实验室建设项目(SZS2024007) （SZS2024007）
苏州市关键核心技术项目(SYG2025124,SYG2025100) （SYG2025124,SYG2025100）
医工所自主部署创新重点项目(E455380101) （E455380101）
