Classification of Left-Versus Right-Hand Motor Imagery in Stroke Patients Using Supplementary Data Generated by CycleGAN
| العنوان: | Classification of Left-Versus Right-Hand Motor Imagery in Stroke Patients Using Supplementary Data Generated by CycleGAN |
|---|---|
| المؤلفون: | Yang Zhang, Jiancai Leng, Fenqi Rong, Fangzhou Xu, Tao Sun, Siddharth Siddharth, Tzyy-Ping Jung |
| المصدر: | IEEE Transactions on Neural Systems and Rehabilitation Engineering. 29:2417-2424 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Stroke patient, Computer science, Biomedical Engineering, Electroencephalography, Convolutional neural network, Motor imagery, Internal Medicine, medicine, Humans, Spatial analysis, Supplementary data, medicine.diagnostic_test, business.industry, General Neuroscience, Deep learning, Rehabilitation, Pattern recognition, Hand, Stroke, Visual inspection, Brain-Computer Interfaces, Imagination, Artificial intelligence, business, Algorithms |
| الوصف: | Acquiring Electroencephalography (EEG) data is often time-consuming, laborious, and costly, posing practical challenges to train powerful but data-demanding deep learning models. This study proposes a surrogate EEG data-generation system based on cycle-consistent adversarial networks (CycleGAN) that can expand the number of training data. This study used EEG2Image based on a modified S-transform (MST) to convert EEG data into EEG-topography. This method retains the frequency-domain characteristics and spatial information of the EEG signals. Then, the CycleGAN is used to learn and generate motor-imagery EEG data of stroke patients. From the visual inspection, there is no difference between the EEG topographies of the generated and original EEG data collected from the stroke patients. Finally, we used convolutional neural networks (CNN) to evaluate and analyze the generated EEG data. The experimental results show that the generated data effectively improved the classification accuracy. |
| تدمد: | 1558-0210 1534-4320 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::357b0037fca574467108965c18c44edfTest https://doi.org/10.1109/tnsre.2021.3123969Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....357b0037fca574467108965c18c44edf |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15580210 15344320 |
|---|