Enhancing EEG Signals in Brain Computer Interface Using Wavelet Transform
Keywords:
Brain computer interface, EEG, wavelet transform, bayes net, SVM, and RBFN.Abstract
Brain-computer interface (BCI) is a hardware and
software communication system that enables humans to
interact with their surrounding without the involvement of
peripheral nerves and muscles by using control signals
generated from electroencephalographic activity. In this paper,
we report on results of developing motor imagery feature
extraction method for BCI. The wavelet coefficients were used
to extract the features from the motor imagery EEG and the
Bayes Net, SVM and RBFN were utilized to classify the pattern
of left, right hand movement and forward imagery. The
performance was tested using dataset from BCI competition III
and satisfactory results are obtained with accuracy rate as high
as 99.0674%.
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