Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Seung-Bo | - |
dc.contributor.author | Kim, Hyun-Ji | - |
dc.contributor.author | Kim, Hakseung | - |
dc.contributor.author | Jeong, Ji-Hoon | - |
dc.contributor.author | Lee, Seong-Whan | - |
dc.contributor.author | Kim, Dong-Joo | - |
dc.date.accessioned | 2021-09-01T05:03:14Z | - |
dc.date.available | 2021-09-01T05:03:14Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2019-10 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/62709 | - |
dc.description.abstract | The electroencephalogram (EEG) remains the predominant source of neurophysiological signals for motor imagery-based brain-computer interfaces (MI-BCIs). Various features can be derived from three distinctive domains (i.e., spatial, temporal and spectral); however, the efficacies of the existing feature extraction methods when discriminating complex multiclass MI tasks have yet to be reported. This study investigates the performances of EEG feature extraction techniques from varying domains against different levels of complex, multiclass MI tasks. Ten healthy volunteers underwent multiple complex MI tasks via a robotic arm (i.e., hand grasping and wrist twisting; grasp, spread, pronation and supination). The discrimination performances of various feature extraction (i.e., common spatial patterns (CSP), time domain parameters (TDP), and power spectral density (PSD)) and classification methods for EEG were tested to perform binary (hand grasping/wrist twisting), ternary ((A) grasp/spread/wrist twisting and (B) hand grasping/pronation/supination) and quaternary (grasp/spread/pronation/supination) discrimination. Based on the available data, the combination of shrinkage-regularized linear discriminant analysis (SRLDA) and TDP achieved the highest accuracy. The findings suggest that multiclass complex MI-BCI task discrimination could gain more benefit from analyzing simple and symbolic features such as TDP rather than more complex features such as CSP and PSD. (C) 2019 Elsevier Inc. All rights reserved. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.subject | ROBOT ARM | - |
dc.subject | PATTERN | - |
dc.subject | INTERFACE | - |
dc.title | Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Hakseung | - |
dc.contributor.affiliatedAuthor | Lee, Seong-Whan | - |
dc.contributor.affiliatedAuthor | Kim, Dong-Joo | - |
dc.identifier.doi | 10.1016/j.ins.2019.06.008 | - |
dc.identifier.scopusid | 2-s2.0-85067252628 | - |
dc.identifier.wosid | 000482494700011 | - |
dc.identifier.bibliographicCitation | INFORMATION SCIENCES, v.502, pp.190 - 200 | - |
dc.relation.isPartOf | INFORMATION SCIENCES | - |
dc.citation.title | INFORMATION SCIENCES | - |
dc.citation.volume | 502 | - |
dc.citation.startPage | 190 | - |
dc.citation.endPage | 200 | - |
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.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | ROBOT ARM | - |
dc.subject.keywordPlus | PATTERN | - |
dc.subject.keywordPlus | INTERFACE | - |
dc.subject.keywordAuthor | Brain-computer interface | - |
dc.subject.keywordAuthor | Electroencephalogram | - |
dc.subject.keywordAuthor | Feature extraction | - |
dc.subject.keywordAuthor | Motor imagery | - |
dc.subject.keywordAuthor | Multiclass discrimination | - |
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