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Med Biol Eng Comput. 2015 Nov;53(11):1201-10. doi: 10.1007/s11517-015-1311-x. Epub 2015 May 16.

Predictive classification of self-paced upper-limb analytical movements with EEG.

Author information

1
Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council - CSIC, Av. Doctor Arce, 37, 28002, Madrid, Spain. jaime.ibanez@csic.es.
2
Neural and Cognitive Engineering Group, Centro de Automática y Robótica, Spanish National Research Council - CSIC, Arganda del Rey, Spain.
3
Aragon Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain.
4
BitBrain Technologies, Zaragoza, Spain.
5
Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council - CSIC, Av. Doctor Arce, 37, 28002, Madrid, Spain.

Abstract

The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5% was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3%). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes.

KEYWORDS:

Brain–computer interface; Data mining; Electroencephalography; Genetic algorithms; Voluntary movements

PMID:
25980505
DOI:
10.1007/s11517-015-1311-x
[Indexed for MEDLINE]

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