Full metadata record
DC Field | Value | Language |
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dc.citation.startPage | 1320457 | - |
dc.citation.title | FRONTIERS IN HUMAN NEUROSCIENCE | - |
dc.citation.volume | 18 | - |
dc.contributor.author | Lee, Jongmin | - |
dc.contributor.author | Kim, Minju | - |
dc.contributor.author | Heo, Dojin | - |
dc.contributor.author | Kim, Jongsu | - |
dc.contributor.author | Kim, Min-Ki | - |
dc.contributor.author | Lee, Taejun | - |
dc.contributor.author | Park, Jongwoo | - |
dc.contributor.author | Kim, HyunYoung | - |
dc.contributor.author | Hwang, Minho | - |
dc.contributor.author | Kim, Laehyun | - |
dc.contributor.author | Kim, Sung-Phil | - |
dc.date.accessioned | 2024-03-13T14:05:12Z | - |
dc.date.available | 2024-03-13T14:05:12Z | - |
dc.date.created | 2024-03-11 | - |
dc.date.issued | 2024-02 | - |
dc.description.abstract | Brain-computer interfaces (BCIs) have a potential to revolutionize human-computer interaction by enabling direct links between the brain and computer systems. Recent studies are increasingly focusing on practical applications of BCIs-e.g., home appliance control just by thoughts. One of the non-invasive BCIs using electroencephalography (EEG) capitalizes on event-related potentials (ERPs) in response to target stimuli and have shown promise in controlling home appliance. In this paper, we present a comprehensive dataset of online ERP-based BCIs for controlling various home appliances in diverse stimulus presentation environments. We collected online BCI data from a total of 84 subjects among whom 60 subjects controlled three types of appliances (TV: 30, door lock: 15, and electric light: 15) with 4 functions per appliance, 14 subjects controlled a Bluetooth speaker with 6 functions via an LCD monitor, and 10 subjects controlled air conditioner with 4 functions via augmented reality (AR). Using the dataset, we aimed to address the issue of inter-subject variability in ERPs by employing the transfer learning in two different approaches. The first approach, "within-paradigm transfer learning," aimed to generalize the model within the same paradigm of stimulus presentation. The second approach, "cross-paradigm transfer learning," involved extending the model from a 4-class LCD environment to different paradigms. The results demonstrated that transfer learning can effectively enhance the generalizability of BCIs based on ERP across different subjects and environments. | - |
dc.identifier.bibliographicCitation | FRONTIERS IN HUMAN NEUROSCIENCE, v.18, pp.1320457 | - |
dc.identifier.doi | 10.3389/fnhum.2024.1320457 | - |
dc.identifier.issn | 1662-5161 | - |
dc.identifier.scopusid | 2-s2.0-85185114045 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/81606 | - |
dc.identifier.wosid | 001161977600001 | - |
dc.language | 영어 | - |
dc.publisher | FRONTIERS MEDIA SA | - |
dc.title | A comprehensive dataset for home appliance control using ERP-based BCIs with the application of inter-subject transfer learning | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Neurosciences; Psychology | - |
dc.relation.journalResearchArea | Neurosciences & Neurology; Psychology | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | ERP-based BCI | - |
dc.subject.keywordAuthor | EEG | - |
dc.subject.keywordAuthor | transfer learning | - |
dc.subject.keywordAuthor | BCI dataset | - |
dc.subject.keywordAuthor | home appliance | - |
dc.subject.keywordPlus | COMPUTER | - |
dc.subject.keywordPlus | VARIABILITY | - |
dc.subject.keywordPlus | GAMES | - |
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