University of Hertfordshire

A method for classifying mental tasks in the space of EEG transforms

Research output: Contribution to conferencePaper

Documents

  • 907274

    Accepted author manuscript, 209 KB, PDF document

  • Renato Cordeiro De Amorim
  • Boris Mirkin
  • John Q. Gan
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Original languageEnglish
Publication statusPublished - 2009
EventUK Workshop on Computational Intelligence - University of Nottingham, Nottingham, United Kingdom
Duration: 7 Sep 20099 Sep 2009

Workshop

WorkshopUK Workshop on Computational Intelligence
CountryUnited Kingdom
CityNottingham
Period7/09/099/09/09

Abstract

In this article we describe a new method for supervised classification of EEG signals. This method applies to the power spectrum density data and assigns
class-dependent information weights to individual pixels, so that the decision is defined by the summary weights of the most informative pixel features. We
experimentally analyze several versions of the approach. The informative features appear to be rather similar among different individuals, thus supporting the view that there are subject independent general brain patterns for the same mental task

Notes

Renato Cordeiro De Amorim, Boris Mirkin, John Q. Gan, ‘A method for classifying mental tasks in the space of EEG transforms’, paper presented at the UK Workshop on Computational Intelligence, Nottingham, UK, 7-9 September, 2009.

ID: 9822528