A local neural classifier for the recognition of EEG patterns associated to mental tasks

José Del R Millán, Josep Mouriño, Marco Franzé, Febo Cincotti, Markus Varsta, Jukka Heikkonen, Fabio Babiloni

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a novel and simple local neural classifier for the recognition of mental tasks from on-line spontaneous EEG signals. The proposed neural classifier recognizes three mental tasks from on-line spontaneous EEG signals. Correct recognition is around 70%. This modest rate is largely compensated by two properties, namely low percentage of wrong decisions (below 5%) and rapid responses (every 1/2 s). Interestingly, the neural classifier achieves this performance with a few units, normally just one per mental task. Also, since the subject and his/her personal interface learn simultaneously from each other, subjects master it rapidly (in a few days of moderate training). Finally, analysis of learned EEG patterns confirms that for a subject to operate satisfactorily a brain interface, the latter must fit the individual features of the former.

Original languageEnglish
Pages (from-to)678-686
Number of pages9
JournalIEEE Transactions on Neural Networks
Volume13
Issue number3
DOIs
Publication statusPublished - May 2002

Keywords

  • Brain-computer interface
  • Local neural classifier
  • Spontaneous EEG activity

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Hardware and Architecture

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