TY - JOUR
T1 - Relevant EEG features for the classification of spontaneous motor-related tasks
AU - Millán, José Del R
AU - Franzé, Marco
AU - Mouriño, Josep
AU - Cincotti, Febo
AU - Babiloni, Fabio
PY - 2002
Y1 - 2002
N2 - 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.
AB - 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.
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U2 - 10.1007/s004220100282
DO - 10.1007/s004220100282
M3 - Article
C2 - 11908842
AN - SCOPUS:0036480598
VL - 86
SP - 89
EP - 95
JO - Biological Cybernetics
JF - Biological Cybernetics
SN - 0340-1200
IS - 2
ER -