Study On The EEG-Based Brain Computer Interface

Many different disorders can disrupt the neuromuscular channels through which the brain communicates with and controls its external environment. Amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury, cerebral palsy, muscular dystrophies, multiple sclerosis, and numerous other diseases impair the neural pathways that control muscles or impair the muscles themselves. They affect nearly 600 thousand people in the United States alone, and far more around the world. Modern life-support technology can allow most individuals, even those who are locked-in, to live long lives, so that the personal, social, and economic burdens of their disabilities are prolonged and severe.In the absence of methods for repairing the damage done by these disorders, there are 3 options for restoring function. The first is to increase the capabilities of remaining pathways. The second option is to restore function by detouring around breaks in the neural pathways that control muscles. The final option for restoring function to those with motor impairments is to provide the brain with a new, non-muscular communication and control channel.Recently research shows that EEG may provide the basis of the new communication channel. EEG activity are recorded at the scalp or single-unit activities recorded within cortex. The EEG- based method of information exchange is try translate the EEG to a new input channel through which the brain communicates and controls with external environment. A brain–computer interface (BCI) is a communication system that does not depend on the brain's normal output pathways of peripheral nerves and muscles.Current research of BCIs mainly focus on provide a new reparative function and the method of communication with external environment for whose motor function deformity but though normal. Over the past decade, the BCIs technology rapid development mainly focus on new control technology based on the EEG activity recorded at the scalp or single-unit activity recorded within cortex. In the research, EEG signal acquisition system is built based on the LabVIEW which is the basis of the online BCIs system. The experiment shows the system is successful and is of great value to control the extended equipment employ the αrhythm. The P300 evoked potentials is detected on the acquisition system by the Oddball stimulate program and realize the primary stage P300-based BCIs system. The dissertation presents the analytical method of Independent Component Analysis (ICA) and Wavelet Transform (WT) for the preprocessing of EEG acquired from the acquisition system. The ICA and WT is used in Single-trial estimation for signal extraction. It is importation for online BCIs system. Four different algorithms is used to discriminate P300: Average, Peak picking, Area, and Correlation. The originalities of this thesis are followings:EEG signal acquisition system is built based on the LabVIEW which is the basis of the online BCIs system and the extended equipment is controlled by the αwave.The P300 potentials is detected on the acquisition system by the Oddball stimulate program and realize the primary stage P300-based BCIs system.Presents the analytical method of the preprocessing for EEG acquired from the P300-based acquisition system and build the model of EEG signal acquisition and processing. It is the basis of P300-based online BCI system.