IEEE Trans Neural Syst Rehabil Eng. 2018 Nov;26(11):2086-2095. doi: 10.1109/TNSRE.2018.2876129. Epub 2018 Oct 18.
Classification of motor imagery electroencephalograph signals is a fundamental problem in brain-computer interface (BCI) systems. We propose in this paper a classification framework based on long short-term memory (LSTM) networks. To achieve robust classification, a one dimension-aggregate approximation (1d-AX) is employed to extract effective signal representation for LSTM networks. Inspired by classical common spatial pattern, channel weighting technique is further deployed to enhance the effectiveness of the proposed classification framework. Public BCI competition data are used for the evaluation of the proposed feature extraction and classification network, whose performance is also compared with that of the state-of-the-arts approaches based on other deep networks.
运动想象脑电信号分类是脑机接口（BCI）系统中的一个基本问题。本文提出了一种基于长短时记忆（LSTM）网络的分类框架。为了实现鲁棒分类，采用一维聚合近似（1d-AX）方法为 LSTM 网络提取有效的信号表示。受经典共空间模式的启发，进一步部署信道加权技术来增强所提出的分类框架的有效性。使用公共 BCI 竞赛数据评估所提出的特征提取和分类网络，并与基于其他深度学习网络的现有方法进行比较。
