Wavelet and Hilbert transform-based Brain Computer Interface

Brain Computer Interface (BCI) is a technology that developed over the last three decades has provided a novel and promising alternative method for interacting with the environment. BCI is a system which translates a subject´s intentions into a control signal for a device, e.g., a computer application, a wheelchair or a neuroprosthesis. Electroencephalogram-based BCI has become a hot spot in the research of neural engineering, rehabilitation, and brain science. The artifacts are disturbance that can occur during the signal acquisition and that can alter the analysis of the signals themselves. Removing artifacts produced in Electroencephalogram (EEG) data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG analysis. In this research, for artifact rejection, EEG data are filtered to the frequency range between 8 and 32 Hz with a butterworth band-pass filter. Finally two different structures of neural network and a support vector machine used to classify features that are extracted by Hilbert and Wavelet transform.