Abstract
Using the EEG Motor Movement/Imagery database there is proposed an off-line analysis for a brain computer interface (BCI) paradigm. The purpose of the quantitative research is to compare classifiers in order to determinate which of them has highest rates of classification. The power spectral density method is used to evaluated the (de)synchronizations that appear on Mu rhythm. The features extracted from EEG signals are classified using linear discriminant classifier (LDA), quadratic classifier (QDA) and classifier based on Mahalanobis distance (MD). The differences between LDA, QDA and MD are small, but the superiority of QDA was sustained by analysis of variance (ANOVA).
Highlights
Brain computer interface (BCI) facilitates a direct communication between brain and an external device
The smallest error of 11,31% was obtained with the quadratic classifier for FC3-FC4, while the largest error 17,62% was obtained with the classifier based on Mahalanobis distance for C3-C4
The errors obtained after applying the quadratic classifier were better than those obtained using linear classifier and classifier based on Mahalanobis distance, both for real and imagined motor task
Summary
Brain computer interface (BCI) facilitates a direct communication between brain and an external device. The artificial intelligence system recognize a certain set of patterns in brain signals following the stages: signal acquisition, preprocessing, feature extraction, classification and the control interface. Different methods such as electroencephalogram (EEG), magnetoencephalogram (MEG), positron emission tomography (PET), single photon emission computed tomography (SPECT) are used in measuring and studying the brain activity. The EEG is the most convenient method used in BCI systems: because it is non-invasive, it has relative low costs, the real-time analysis may be performed and can be used in a portable device. EEG based BCIs use a set of sensors that pick up the EEG signals from different brain areas
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