A Support Vector Machine (SVM) classification method for data acquired by EEG registration for brain/computer interface systems is here proposed. The aim of this work is to evaluate the SVM performances in the recognition of a human mental task, among others. Such methodology could be very useful in important applications for disabled people. A prerequisite has been the developing of a system capable to recognize and classify the following four tasks: thinking to move the right hand, thinking to move the left hand, performing a simple mathematical operation, and thinking to a nursery rhyme. The data set exploited in the training and testing phases has been acquired by means of 61 EEG electrodes and consists of several time series. These time data sets were then transformed into the frequency domain, in order to obtain the power frequency spectrum. In such a way, for every electrode, 128 frequency channels were obtained. Finally, the SVM algorithm was used and evaluated to get the proposed classification.
Salerno, M., Costantini, G., Casali, D., Orengo, G., Cavallo, P., Saggio, G., et al. (2010). SVM evaluation for brain computer interface systems. ??????? it.cilea.surplus.oa.citation.tipologie.CitationProceedings.prensentedAt ??????? International conference on bio-inspired systems and signal processing.
SVM evaluation for brain computer interface systems
SALERNO, MARIO;COSTANTINI, GIOVANNI;ORENGO, GIANCARLO;SAGGIO, GIOVANNI;BIANCHI, LUIGI;
2010-01-01
Abstract
A Support Vector Machine (SVM) classification method for data acquired by EEG registration for brain/computer interface systems is here proposed. The aim of this work is to evaluate the SVM performances in the recognition of a human mental task, among others. Such methodology could be very useful in important applications for disabled people. A prerequisite has been the developing of a system capable to recognize and classify the following four tasks: thinking to move the right hand, thinking to move the left hand, performing a simple mathematical operation, and thinking to a nursery rhyme. The data set exploited in the training and testing phases has been acquired by means of 61 EEG electrodes and consists of several time series. These time data sets were then transformed into the frequency domain, in order to obtain the power frequency spectrum. In such a way, for every electrode, 128 frequency channels were obtained. Finally, the SVM algorithm was used and evaluated to get the proposed classification.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.