Show simple item record

dc.contributor.authorKumar, Shiu
dc.contributor.authorTsunoda, Tatsuhiko
dc.contributor.authorSharma, Alok
dc.date.accessioned2021-06-08T05:41:41Z
dc.date.available2021-06-08T05:41:41Z
dc.date.issued2021
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/s12859-021-04091-x
dc.identifier.urihttp://hdl.handle.net/10072/405001
dc.description.abstractBACKGROUND: Brain wave signal recognition has gained increased attention in neuro-rehabilitation applications. This has driven the development of brain-computer interface (BCI) systems. Brain wave signals are acquired using electroencephalography (EEG) sensors, processed and decoded to identify the category to which the signal belongs. Once the signal category is determined, it can be used to control external devices. However, the success of such a system essentially relies on significant feature extraction and classification algorithms. One of the commonly used feature extraction technique for BCI systems is common spatial pattern (CSP). RESULTS: The performance of the proposed spatial-frequency-temporal feature extraction (SPECTRA) predictor is analysed using three public benchmark datasets. Our proposed predictor outperformed other competing methods achieving lowest average error rates of 8.55%, 17.90% and 20.26%, and highest average kappa coefficient values of 0.829, 0.643 and 0.595 for BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, respectively. CONCLUSIONS: Our proposed SPECTRA predictor effectively finds features that are more separable and shows improvement in brain wave signal recognition that can be instrumental in developing improved real-time BCI systems that are computationally efficient.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofpagefrom195
dc.relation.ispartofissueSuppl 6
dc.relation.ispartofjournalBMC Bioinformatics
dc.relation.ispartofvolume22
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchcode01
dc.subject.fieldofresearchcode06
dc.subject.fieldofresearchcode08
dc.subject.keywordsBrain computer interface (BCI)
dc.subject.keywordsCommon spatial pattern (CSP)
dc.subject.keywordsCommon spatio-spectral pattern (CSSP)
dc.subject.keywordsElectroencephalography (EEG)
dc.subject.keywordsMotor imagery (MI)
dc.titleSPECTRA: a tool for enhanced brain wave signal recognition.
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationKumar, S; Tsunoda, T; Sharma, A, SPECTRA: a tool for enhanced brain wave signal recognition., BMC Bioinformatics, 2021, 22 (Suppl 6), pp. 195
dcterms.dateAccepted2021-03-21
dcterms.licensehttps://creativecommons.org/licenses/by/4.0/
dc.date.updated2021-06-08T03:05:04Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© The Author(s), 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record