Convolutional neural network based on recurrence plot for EEG recognition
العنوان: | Convolutional neural network based on recurrence plot for EEG recognition |
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المؤلفون: | Chongqing Hao, Ruiqi Wang, Mengyu Li, Chao Ma, Qing Cai, Zhongke Gao |
المصدر: | Chaos: An Interdisciplinary Journal of Nonlinear Science. 31:123120 |
بيانات النشر: | AIP Publishing, 2021. |
سنة النشر: | 2021 |
مصطلحات موضوعية: | Epilepsy, Seizures, Applied Mathematics, Brain, Humans, General Physics and Astronomy, Electroencephalography, Statistical and Nonlinear Physics, Neural Networks, Computer, Mathematical Physics |
الوصف: | 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. |
تدمد: | 1089-7682 1054-1500 |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c870a9a7d1ebc0bcefd86a076237ee2 https://doi.org/10.1063/5.0062242 |
رقم الأكسشن: | edsair.doi.dedup.....8c870a9a7d1ebc0bcefd86a076237ee2 |
قاعدة البيانات: | OpenAIRE |
تدمد: | 10897682 10541500 |
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