Relevant EEG features for the classification of spontaneous motor-related tasks

José Del R Millán, Marco Franzé, Josep Mouriño, Febo Cincotti, Fabio Babiloni

Research output: Contribution to journalArticle

Abstract

There is a growing interest in the use of physiological signals for communication and operation of devices for the severely motor disabled as well as for healthy people. A few groups around the world have developed brain-computer interfaces (BCIs) that rely upon the recognition of motor-related tasks (i.e., imagination of movements) from on-line EEG signals. In this paper we seek to find and analyze the set of relevant EEG features that best differentiate spontaneous motor-related mental tasks from each other. This study empirically demonstrates the benefits of heuristic feature selection methods for EEG-based classification of mental tasks. In particular, it is shown that the classifier performance improves for all the considered subjects with only a small proportion of features. Thus, the use of just those relevant features increases the efficiency of the brain interfaces and, most importantly, enables a greater level of adaptation of the personal BCI to the individual user.

Original languageEnglish
Pages (from-to)89-95
Number of pages7
JournalBiological Cybernetics
Volume86
Issue number2
DOIs
Publication statusPublished - 2002

ASJC Scopus subject areas

  • Biophysics

Fingerprint Dive into the research topics of 'Relevant EEG features for the classification of spontaneous motor-related tasks'. Together they form a unique fingerprint.

  • Cite this