Performance evaluation of classifiers in distinguishing mental tasks from EEG signals

Isaak Kavasidis, Carmelo Pino, Concetto Spampinato, Francesco Maiorana, Giuseppe Lanza

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

During the last decade a lot of research has been done to study the possibility of voluntarily controlling machines by EEG signals. To accomplish that, very accurate recognition of the intended task is needed and so, given the variability of the human brain's signals, discriminant features and high performance classifiers are demanded. In this paper, a study on the performance of different classifiers for distinguishing three mental tasks using EEG signals, is presented. The acquired EEG data is filtered and processed, and a set of nine features about power, the signals' synchronization, and instantaneous frequency are extracted. Classification performance was analyzed across subjects using seventeen individual classifiers. The results obtained from each classifier were also combined in order to evaluate both the efficiency of individual classifiers and the use of combiners.

Original languageEnglish
Title of host publicationProceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012
Pages171-177
Number of pages7
DOIs
Publication statusPublished - 2012
Event9th IASTED International Conference on Biomedical Engineering, BioMed 2012 - Innsbruck, Austria
Duration: Feb 15 2012Feb 17 2012

Other

Other9th IASTED International Conference on Biomedical Engineering, BioMed 2012
CountryAustria
CityInnsbruck
Period2/15/122/17/12

Keywords

  • Brain computer interface
  • Classifiers
  • Combiners
  • Performance evaluation

ASJC Scopus subject areas

  • Biomedical Engineering

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