计算机科学
黎曼几何
人工智能
黎曼流形
歧管(流体力学)
领域(数学分析)
协方差
脑-机接口
模式识别(心理学)
深度学习
适应(眼睛)
域适应
算法
脑电图
数学
几何学
数学分析
统计
工程类
物理
光学
精神科
分类器(UML)
机械工程
心理学
作者
Wenchao Liu,Chang Gao,Chang Gao
标识
DOI:10.1016/j.eswa.2023.121612
摘要
Recently, more and more studies have begun to use deep learning to decode and classify EEG signals. The use of deep learning has led to an increase in the classification accuracy of motor imagery (MI), but the problem of taking a long time to calibrate in brain-computer interface (BCI) applications has not been solved. To address this problem, we propose a novel Riemannian geometry and deep domain adaptation network (RGDDANet) for MI classification. Specifically, two one-dimensional convolutions are designed to extract temporal and spatial features from the EEG signals, and then the spatial covariance matrices are utilized to map the extracted features to Riemannian manifolds for processing. In order to align the source and target features’ distributions on the Riemannian manifold, we propose a Symmetric Positive Definite (SPD) matrix mean discrepancy loss (SMMDL) to minimize the distance between two domains. To analyze the feasibility of the method, we conducted extensive experiments on BCIC IV 2a and BCIC IV 2b datasets, respectively, and the results showed that the proposed method achieved better performance than some state-of-the-art methods.
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