论文标题
马特：用于脑电图解码的多种注意网络
MAtt: A Manifold Attention Network for EEG Decoding
论文作者
论文摘要
脑电图（EEG）信号的识别高度影响非侵入性脑部计算机界面（BCIS）的效率。尽管最新的深度学习（DL）EEG解码器提供了改进的性能，但几何学习的发展（GL）引起了极大的关注，以在解码嘈杂的EEG数据方面提供出色的鲁棒性。但是，缺乏关于对脑电图解码的合并使用深度神经网络（DNN）和几何学习的研究。我们在这里提出了一个基于新型的几何深度学习（GDL）模型的多种注意力网络（Matt），其具有多种注意机制，该机制表征了EEG数据的时空表示，完全在Riemannian对称的正面确定（SPD）上。对时间同步和 - 同时性EEG数据集的提议Matt的评估表明，它优于其他领先的DL方法，用于一般的EEG解码。此外，对模型解释的分析揭示了马特在捕获信息性的脑电图特征和处理脑动力学的非平稳性方面的能力。
Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL)-based EEG decoders offer improved performances, the development of geometric learning (GL) has attracted much attention for offering exceptional robustness in decoding noisy EEG data. However, there is a lack of studies on the merged use of deep neural networks (DNNs) and geometric learning for EEG decoding. We herein propose a manifold attention network (mAtt), a novel geometric deep learning (GDL)-based model, featuring a manifold attention mechanism that characterizes spatiotemporal representations of EEG data fully on a Riemannian symmetric positive definite (SPD) manifold. The evaluation of the proposed MAtt on both time-synchronous and -asyncronous EEG datasets suggests its superiority over other leading DL methods for general EEG decoding. Furthermore, analysis of model interpretation reveals the capability of MAtt in capturing informative EEG features and handling the non-stationarity of brain dynamics.
