计算机科学
脑电图
模式识别(心理学)
人工智能
卷积神经网络
人工神经网络
重现图
脑-机接口
特征提取
作者
Chongqing Hao,Ruiqi Wang,Li Ming-Yang,Chao Ma,Qing Cai,Zhong-Ke Gao
出处
期刊:Chaos
[American Institute of Physics]
日期:2021-12-01
卷期号:31 (12): 123120-123120
摘要
Electroencephalogram (EEG) is a typical physiological signal. The classification of EEG signals is of great significance to human beings. Combining recurrence plot and convolutional neural network (CNN), we develop a novel method for classifying EEG signals. We select two typical EEG signals, namely, epileptic EEG and fatigue driving EEG, to verify the effectiveness of our method. We construct recurrence plots from EEG signals. Then, we build a CNN framework to classify the EEG signals under different brain states. For the classification of epileptic EEG signals, we design three different experiments to evaluate the performance of our method. The results suggest that the proposed framework can accurately distinguish the normal state and the seizure state of epilepsy. Similarly, for the classification of fatigue driving EEG signals, the method also has a good classification accuracy. In addition, we compare with the existing methods, and the results show that our method can significantly improve the detection results.
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