SessionNet: Feature Similarity-Based Weighted Ensemble Learning for Motor Imagery Classification
DC Field | Value | Language |
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dc.contributor.author | Lee, Byeong-Hoo | - |
dc.contributor.author | Jeong, Ji-Hoon | - |
dc.contributor.author | Lee, Seong-Whan | - |
dc.date.accessioned | 2021-08-31T15:58:23Z | - |
dc.date.available | 2021-08-31T15:58:23Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/58933 | - |
dc.description.abstract | A brain-computer interface (BCI) provides a direct communication pathway between user and external devices. Motor imagery (MI) paradigm is widely used in non-invasive BCI to control external devices by decoding user intentions. The traditional MI-BCI problem is to obtain enough EEG data samples for adopting deep learning techniques, as electroencephalography (EEG) data have intricate and non-stationary properties that can cause a discrepancy between different sessions of data. Because of the discrepancy, the recorded EEG data with different sessions cannot be treated as the same. In this study, we recorded a large intuitive EEG dataset that contained nine types of movements of a single-arm across 12 subjects. We proposed a SessionNet that learns generality with EEG data recorded over multiple sessions using feature similarity to improve classification performance. Additionally, the SessionNet adopts the principle of a hierarchical convolutional neural network that shows robust classification performance regardless of the number of classes. The SessionNet outperforms conventional methods on 3-class, 5-class, and two types of 7-class and 9-class of a single-arm task. Hence, our approach could demonstrate the possibility of using feature similarity based on a novel ensemble learning method to train generality from multiple session data for better MI classification performance. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | CONVOLUTIONAL NEURAL-NETWORKS | - |
dc.subject | EEG CLASSIFICATION | - |
dc.subject | SUBJECT | - |
dc.subject | ERROR | - |
dc.title | SessionNet: Feature Similarity-Based Weighted Ensemble Learning for Motor Imagery Classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Seong-Whan | - |
dc.identifier.doi | 10.1109/ACCESS.2020.3011140 | - |
dc.identifier.scopusid | 2-s2.0-85089307295 | - |
dc.identifier.wosid | 000554358600001 | - |
dc.identifier.bibliographicCitation | IEEE ACCESS, v.8, pp.134524 - 134535 | - |
dc.relation.isPartOf | IEEE ACCESS | - |
dc.citation.title | IEEE ACCESS | - |
dc.citation.volume | 8 | - |
dc.citation.startPage | 134524 | - |
dc.citation.endPage | 134535 | - |
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 | Engineering | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordPlus | CONVOLUTIONAL NEURAL-NETWORKS | - |
dc.subject.keywordPlus | EEG CLASSIFICATION | - |
dc.subject.keywordPlus | SUBJECT | - |
dc.subject.keywordPlus | ERROR | - |
dc.subject.keywordAuthor | Electroencephalography | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Indexes | - |
dc.subject.keywordAuthor | Visualization | - |
dc.subject.keywordAuthor | Training | - |
dc.subject.keywordAuthor | Protocols | - |
dc.subject.keywordAuthor | Brain-computer interface (BCI) | - |
dc.subject.keywordAuthor | electroencephalogram (EEG) | - |
dc.subject.keywordAuthor | motor imagery (MI) | - |
dc.subject.keywordAuthor | convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | weighted ensemble learning | - |
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