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Brain computer interface is one of the most recent and latest hot field in Computer Science which emerged in order to help some handicapped people. This paper investigates different classification algorithms that deal with the BCI P300 speller diagram. The system used is composed of an ensemble of Support vector machines. Three different methods are used, namely weighted ensemble of SVM, channel selection...
In BCI research community, support vector machine (SVM) is an effective method for motor imagery (MI)-based electroencephalographic (EEG) classification. However, the computation of decision function during SVM classification stage for a new EEG trial is time-consuming due to the large number of support vectors (SV). This paper proposes a new method to reduce the number of support vectors so that...
A Support Vector Machine (SVM) classification method for data acquired by EEG recording for brain/computer interface systems is here proposed. The aim of this work is to evaluate the SVM performance in the recognition of a human mental task, among others. A prerequisite has been the developing of a system able to recognize and classify the following four tasks: thinking to move the right hand, thinking...
Brain-computer interface P300 speller aims at helping patients unable to activate muscles to spell words by means of their brain signal activities. Associated to this BCI paradigm, there is the problem of classifying electroencephalogram signals related to responses to some visual stimuli. This paper addresses the problem of signal responses variability within a single subject in such brain-computer...
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