Deep recurrent spatio-temporal neural network for motor imagery based BCI

Wonjun Ko, Jee Seok Yoon, Eunsong Kang, E. Jun, Jun-Sik Choi, Heung-Il Suk
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引用次数: 24

Abstract

In this paper, we propose a novel architecture of a deep neural network for EEG-based motor imagery classification. Unlike the existing deep neural networks in the literature, the proposed network allows us to analyze the learned network weights from a neurophysiological perspective, thus providing an insight into the underlying patterns inherent in motor imagery induced EEG signals. In order to validate the effectiveness of the proposed method, we conducted experiments on the BCI Competition IV-IIa dataset by comparing with the competing methods in terms of the Cohen's k value. For qualitative analysis, we also performed visual inspection of the activation patterns estimated from the learned network weights.
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基于运动意象的脑机接口深度递归时空神经网络
在本文中,我们提出了一种新的基于脑电图的运动图像分类的深度神经网络架构。与文献中现有的深度神经网络不同,本文提出的网络允许我们从神经生理学的角度分析学习到的网络权重,从而深入了解运动图像诱发的脑电图信号的内在模式。为了验证所提出方法的有效性,我们在BCI Competition IV-IIa数据集上进行了实验,并在Cohen’s k值方面与竞争方法进行了比较。为了进行定性分析,我们还对从学习到的网络权重估计的激活模式进行了视觉检查。
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