发表机构
School of Science（理学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出CNN与bi-LSTM结合的混合深度学习架构，用于MI-EEG解码，在公开与私有数据集上实现稳健的二、三类运动想象分类，具备良好的被试独立解码能力。
AI 中文摘要
运动想象（MI）脑机接口（BCI）已成为建立人脑与外部设备灵活通信通路的有前景方法，尤其适用于中风或神经退行性疾病患者。可靠解码运动想象脑电（MI-EEG）仍具挑战性，因为脑电记录包含大量噪声，且与潜在脑活动存在复杂、弱信息关联。尽管深度学习为直接从脑电信号学习表征提供了有效手段，但其在MI-EEG特征学习中的应用仍相对有限。本研究提出一种混合深度学习架构，将卷积神经网络（CNN）与双向长短期记忆（bi-LSTM）网络相结合：CNN用于直接从原始MI-EEG记录学习高级空间与时间表征，bi-LSTM则对提取特征间的时间依赖关系与关联进行建模。该方法通过公开数据集和使用某脑电采集系统获取的私有数据集进行评估，实验结果表明，CNN&bi-LSTM架构在二类和三类运动想象分类任务中均表现出稳健性能，且在所有评估方法中展现出良好的被试独立解码能力。
英文摘要
Motor imagery (MI) brain--computer interfaces (BCIs) have emerged as a promising approach for establishing flexible communication pathways between the human brain and external devices , particularly for individuals affected by stroke or neurodegenerative disorders. Reliable decoding of motor-imagery electroencephalography (MI-EEG) remains challenging because EEG recordings contain substantial noise and exhibit complex, weakly informative relationships with the underlying brain activity. Although deep learning provides an effective means of learning representations directly from EEG signals, its application to MI-EEG feature learning remains comparatively limited. This study introduces a hybrid deep-learning architecture that integrates a convolutional neural network (CNN) with a bidirectional long short-term memory (bi-LSTM) network. The CNN is used to learn high-level spatial and temporal representations directly from raw MI-EEG recordings, whereas the bi-LSTM models temporal dependencies and relationships among the extracted features. The proposed approach is evaluated using both a publicly available dataset and a privately acquired dataset obtained with an EEG acquisition system. The experimental results indicate that the CNN\&bi-LSTM architecture provides robust performance for both two- and three-class motor-imagery classification and demonstrates promising subject-independent decoding capability across the evaluated methods.
Comments9 pages, 9 figures
