Research on classification of Motor Imagery EEG signal based on CNN architecture

Yingjie Zhang, Xiaozhong Geng, Hui Yan
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Abstract

BCI based on machine learning could makes use of the EEG signals to communicate to output under the condition of without the participation of peripheral nerves and muscles. Extracting the essential features of the EEG signals in the presence of artifacts, training the classification algorithms and optimizing the performance of classifier is critical procedure for BCI system. In the realization of BCI, the most important step is the feature extraction and classification of EEG signals. Due to the obvious individual difference and low signal-to-noise ratio of EEG signals, the current feature extraction and classification algorithms have low accuracy. The emergence of deep learning has attracted much attention in many fields. At present, some researchers try to apply deep learning algorithm to the recognition of EEG signals, and obtain good results. Based on convolutional Neural Networks (CNN), this paper studies the application of deep learning in motor imagery task classification by end-to-end deep learning.
基于CNN架构的运动意象脑电信号分类研究
基于机器学习的脑机接口可以在没有外周神经和肌肉参与的情况下,利用脑电信号进行交流输出。提取存在伪影的脑电信号的本质特征,训练分类算法和优化分类器性能是脑机接口系统的关键步骤。在脑机接口的实现中,最重要的一步是脑电信号的特征提取和分类。由于脑电信号具有明显的个体差异和较低的信噪比,目前的特征提取和分类算法准确率较低。深度学习的出现引起了很多领域的关注。目前,有研究者尝试将深度学习算法应用于脑电信号的识别,并取得了良好的效果。基于卷积神经网络(CNN),通过端到端深度学习,研究了深度学习在运动意象任务分类中的应用。
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