A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification
| العنوان: | A three-branch 3D convolutional neural network for EEG-based different hand movement stages classification |
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| المؤلفون: | Tianjun Liu, Deling Yang |
| المصدر: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) |
| بيانات النشر: | Nature Publishing Group UK, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Computer science, Science, Movement, 0206 medical engineering, 02 engineering and technology, Electroencephalography, Convolutional neural network, Article, Motor imagery, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, Representation (mathematics), Spatial analysis, Multidisciplinary, medicine.diagnostic_test, business.industry, Deep learning, Pattern recognition, Hand, 020601 biomedical engineering, Class (biology), Data set, ComputingMethodologies_PATTERNRECOGNITION, Neurology, Brain-Computer Interfaces, Medicine, 020201 artificial intelligence & image processing, Artificial intelligence, Neural Networks, Computer, business, Neuroscience |
| الوصف: | Motor Imagery is a classical method of Brain Computer Interaction, in which electroencephalogram (EEG) signal features evoked by the imaginary body movements are recognized, and relevant information is extracted. Recently, various deep learning methods are being focused on finding an easy-to-use EEG representation method that can preserve both temporal information as well as spatial information. To further utilize the spatial and temporal features of EEG signals, we proposed a 3D representation of EEG and an end-to-end EEG three-branch 3D convolutional neural network, to address the class imbalance problem (dataset show unequal distribution among their classes), we proposed a class balance cropped strategy. Experimental results indicated that there are also a problem of the different classification difficulty for different classes in motor stages classification tasks, we introduce focal loss to address problem of ‘easy-hard’ examples, when trained with the focal loss, the three-branch 3D-CNN network achieve good performance (relatively more balanced classification accuracy of binary classifications) on the WAY-EEG-GAL data set. Experimental results show that the proposed method is a good method, which can improve classification effect of different motor stages classification. |
| اللغة: | English |
| تدمد: | 2045-2322 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44497086a53beb4e44155cf220b92722Test http://europepmc.org/articles/PMC8144431Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....44497086a53beb4e44155cf220b92722 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20452322 |
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