Towards and Adaptive Brain- Computer Interface- An Error Potential Approach

  • Yazar/lar FIGUEIREDO, Nuno
    SILVA, Filipe
    GEORGIEVA, Petia
    MILANOVA, Mariofanna
    MENDİ, Engin
  • Yayın Türü Konferans Bildirisi
  • Yayın Tarihi 2015
  • DOI Numarası 10.1007/978-3-319-14899-1_12
  • Yayıncı Springer Verlag
  • Tek Biçim Adres http://hdl.handle.net/20.500.12498/3109

In this paper a new adaptive Brain Computer Interface (BCI) architecture is proposed that allows to autonomously adapt the BCI parameters in malfunctioning situations. Such situations are detected by discriminating EEG Error Potentials and when necessary the BCI mode is switched back to the training stage in order to improve its performance. First, the modules of the adaptive BCI are presented, then the scenarios for identification of the user reaction to intentionally introduced errors are discussed and finally promising preliminary results are commented. The proposed concept has the potential to increase the reliability of BCI systems. © Springer International Publishing Switzerland 2015.

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Eser Adı
(dc.title)
Towards and Adaptive Brain- Computer Interface- An Error Potential Approach
Yayın Türü
(dc.type)
Konferans Bildirisi
Yazar/lar
(dc.contributor.author)
FIGUEIREDO, Nuno
Yazar/lar
(dc.contributor.author)
SILVA, Filipe
Yazar/lar
(dc.contributor.author)
GEORGIEVA, Petia
Yazar/lar
(dc.contributor.author)
MILANOVA, Mariofanna
Yazar/lar
(dc.contributor.author)
MENDİ, Engin
DOI Numarası
(dc.identifier.doi)
10.1007/978-3-319-14899-1_12
Atıf Dizini
(dc.source.database)
Scopus
Yayıncı
(dc.publisher)
Springer Verlag
Yayın Tarihi
(dc.date.issued)
2015
Kayıt Giriş Tarihi
(dc.date.accessioned)
2020-08-07T13:00:40Z
Açık Erişim tarihi
(dc.date.available)
2020-08-07T13:00:40Z
Kaynak
(dc.source)
3rd IAPR TC3 Workshop on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2014
ISSN
(dc.identifier.issn)
03029743 (ISSN); 9783319148984 (ISBN)
Özet
(dc.description.abstract)
In this paper a new adaptive Brain Computer Interface (BCI) architecture is proposed that allows to autonomously adapt the BCI parameters in malfunctioning situations. Such situations are detected by discriminating EEG Error Potentials and when necessary the BCI mode is switched back to the training stage in order to improve its performance. First, the modules of the adaptive BCI are presented, then the scenarios for identification of the user reaction to intentionally introduced errors are discussed and finally promising preliminary results are commented. The proposed concept has the potential to increase the reliability of BCI systems. © Springer International Publishing Switzerland 2015.
Yayın Dili
(dc.language.iso)
en
Tek Biçim Adres
(dc.identifier.uri)
http://hdl.handle.net/20.500.12498/3109
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