Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Sung, Dong-Jin | - |
dc.contributor.author | Kim, Keun-Tae | - |
dc.contributor.author | Jeong, Ji Hyeok | - |
dc.contributor.author | Kim, Lae hyun | - |
dc.contributor.author | Lee, Song Joo | - |
dc.contributor.author | Kim, Hyungmin | - |
dc.contributor.author | Kim, Seung-Jong | - |
dc.date.accessioned | 2024-09-19T00:00:22Z | - |
dc.date.available | 2024-09-19T00:00:22Z | - |
dc.date.created | 2024-09-14 | - |
dc.date.issued | 2024-09 | - |
dc.identifier.uri | https://pubs.kist.re.kr/handle/201004/150605 | - |
dc.description.abstract | Motor imagery (MI)-based brain-computer interfaces (BCIs) using electroencephalography (EEG) have found practical applications in external device control. However, the non-stationary nature of EEG signals remains to obstruct BCI performance across multiple sessions, even for the same user. In this study, we aim to address the impact of non-stationarity, also known as inter-session variability, on multi-session MI classification performance by introducing a novel approach, the relevant session-transfer (RST) method. Leveraging the cosine similarity as a benchmark, the RST method transfers relevant EEG data from the previous session to the current one. The effectiveness of the proposed RST method was investigated through performance comparisons with the self-calibrating method, which uses only the data from the current session, and the whole-session transfer method, which utilizes data from all prior sessions. We validated the effectiveness of these methods using two datasets: a large MI public dataset (Shu Dataset) and our own dataset of gait-related MI, which includes both healthy participants and individuals with spinal cord injuries. Our experimental results revealed that the proposed RST method leads to a 2.29 % improvement (p < 0.001) in the Shu Dataset and up to a 6.37 % improvement in our dataset when compared to the self-calibrating method. Moreover, our method surpassed the performance of the recent highest-performing method that utilized the Shu Dataset, providing further support for the efficacy of the RST method in improving multi-session MI classification performance. Consequently, our findings confirm that the proposed RST method can improve classification performance across multiple sessions in practical MI-BCIs. | - |
dc.language | English | - |
dc.publisher | Cell Press | - |
dc.title | Improving inter-session performance via relevant session-transfer for multi-session motor imagery classification | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.heliyon.2024.e37343 | - |
dc.description.journalClass | 1 | - |
dc.identifier.bibliographicCitation | Heliyon, v.10, no.17 | - |
dc.citation.title | Heliyon | - |
dc.citation.volume | 10 | - |
dc.citation.number | 17 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.identifier.wosid | 001310473800001 | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | EEG | - |
dc.subject.keywordPlus | BCI | - |
dc.subject.keywordPlus | DOMAIN ADAPTATION NETWORK | - |
dc.subject.keywordAuthor | Session-transfer approach | - |
dc.subject.keywordAuthor | Cosine similarity | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | Brain-computer interface | - |
dc.subject.keywordAuthor | Gait-related motor imagery | - |
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