Relevant EEG features for the classification of spontaneous motor-related tasks |
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Authors: | José del R Millán Marco Franzé Josep Mouriño Febo Cincotti Fabio Babiloni |
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Institution: | (1) ISIS, Joint Research Centre of the EC, 21020 Ispra, Italy, IT;(2) Fondazione Santa Lucia, Via Ardeatina 306, 00179 Rome, Italy, IT;(3) Dipartimenti Fisiologia Umana e Farmacologia, Università La Sapienza, P. le A. Moro 5, 00185 Rome, Italy, IT |
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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.
Received: 15 January 2001 / Accepted in revised form: 19 July 2001 |
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