Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, K. | - |
dc.contributor.author | Robinson, N. | - |
dc.contributor.author | Lee, S.-W. | - |
dc.contributor.author | Guan, C. | - |
dc.date.accessioned | 2021-12-02T23:42:46Z | - |
dc.date.available | 2021-12-02T23:42:46Z | - |
dc.date.created | 2021-08-31 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 0893-6080 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/128992 | - |
dc.description.abstract | In recent years, deep learning has emerged as a powerful tool for developing Brain–Computer Interface (BCI) systems. However, for deep learning models trained entirely on the data from a specific individual, the performance increase has only been marginal owing to the limited availability of subject-specific data. To overcome this, many transfer-based approaches have been proposed, in which deep networks are trained using pre-existing data from other subjects and evaluated on new target subjects. This mode of transfer learning however faces the challenge of substantial inter-subject variability in brain data. Addressing this, in this paper, we propose 5 schemes for adaptation of a deep convolutional neural network (CNN) based electroencephalography (EEG)-BCI system for decoding hand motor imagery (MI). Each scheme fine-tunes an extensively trained, pre-trained model and adapt it to enhance the evaluation performance on a target subject. We report the highest subject-independent performance with an average (N=54) accuracy of 84.19% (±9.98%) for two-class motor imagery, while the best accuracy on this dataset is 74.15% (±15.83%) in the literature. Further, we obtain a statistically significant improvement (p=0.005) in classification using the proposed adaptation schemes compared to the baseline subject-independent model. © 2020 Elsevier Ltd | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Ltd | - |
dc.subject | Brain computer interface | - |
dc.subject | Convolution | - |
dc.subject | Convolutional neural networks | - |
dc.subject | Deep neural networks | - |
dc.subject | Electroencephalography | - |
dc.subject | Electrophysiology | - |
dc.subject | Image classification | - |
dc.subject | Learning systems | - |
dc.subject | Transfer learning | - |
dc.subject | Adaptation scheme | - |
dc.subject | Evaluation performance | - |
dc.subject | Hand motor imageries | - |
dc.subject | Independent model | - |
dc.subject | Learning models | - |
dc.subject | Motor imagery | - |
dc.subject | Motor imagery classification | - |
dc.subject | Subject-specific | - |
dc.subject | Deep learning | - |
dc.title | Adaptive transfer learning for EEG motor imagery classification with deep Convolutional Neural Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, S.-W. | - |
dc.identifier.doi | 10.1016/j.neunet.2020.12.013 | - |
dc.identifier.scopusid | 2-s2.0-85099248925 | - |
dc.identifier.wosid | 000632800900001 | - |
dc.identifier.bibliographicCitation | Neural Networks, v.136, pp.1 - 10 | - |
dc.relation.isPartOf | Neural Networks | - |
dc.citation.title | Neural Networks | - |
dc.citation.volume | 136 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 10 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Neurosciences & Neurology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Neurosciences | - |
dc.subject.keywordPlus | Brain computer interface | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Electroencephalography | - |
dc.subject.keywordPlus | Electrophysiology | - |
dc.subject.keywordPlus | Image classification | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Transfer learning | - |
dc.subject.keywordPlus | Adaptation scheme | - |
dc.subject.keywordPlus | Evaluation performance | - |
dc.subject.keywordPlus | Hand motor imageries | - |
dc.subject.keywordPlus | Independent model | - |
dc.subject.keywordPlus | Learning models | - |
dc.subject.keywordPlus | Motor imagery | - |
dc.subject.keywordPlus | Motor imagery classification | - |
dc.subject.keywordPlus | Subject-specific | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordAuthor | Brain–computer interface (BCI) | - |
dc.subject.keywordAuthor | Convolutional Neural Network (CNN) | - |
dc.subject.keywordAuthor | Electroencephalography (EEG) | - |
dc.subject.keywordAuthor | Transfer learning | - |
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