EEG Signals Analysis for motor imagery based on Curvelet Transform (International Journal of Advanced Research in Computer Science, 2017)
EEG-based brain-computer interface is a computer-based system provides effective communication and control channels between human brain and computer to carry out a desired action. However, classification of single-trial EEG signals and controlling a device continuously during motor imagery is a difficult task. In this paper, we propose feature extraction method for a single trial online motor imagery using curvelet transform. These curvelet coefficients were used to extract the characters from the motor imagery EEG and classify the pattern of left and right hand movement imagery by Bayesian analysis with Gaussian model. The performance of motor imagery tested by the eye dataset for BCI competition 2003. The hypothetical results presented highest classification accuracy of 96% and superior information transfer rate is obtained.Keywords: Electroencephalograph (EEG), Curvelet coefficients, Motor imagery, Bayesian classifier, Gaussian model, Brain-computer interface (BCI).