Research Of 3D Image Control System Based On SSVEP

A brain computer interface(BCI) or a brain machine interface(BMI) is a direct communication or control path between the User and the Device Controller. In this definition, the "brain" is meant EEG(Electroencephalograph) produced by the brain, while the "machine" refers to a computer or mechanical device capable of receiving and processing EEG.A BCI establishes a communication or control system which does not depend on the brain’s normal output channels. That is, the message is not carried by nerves and muscles, and, furthermore, neuromuscular activity is not needed to produce the activity that does carry the message. Through this definition, BCI systems appear to be a possible and sometimes unique mode of communication or control for people who can not use the brain’s normal output channels. It can help people with severe neuromuscular disorders like spinal cord injury, amyotrophic lateral sclerosis(ALS), multiple sclerosis(MS) or cerebral palsy, which can break the fragile line between thoughts and actions because they interrupt or otherwise impair the the pathways that control motor neurons in the spinal cord and the brainstem and therefore produce movement. This is the initial, and moreover the most important motivation of BCI research.The most critical issue in BCI research is how to produce and distinguish the EEG. Since the 1990 s, much exciting progress has been made on BCIs. To create a BCI, one can use various sources of information from the EEG. Over the last 20 years, brain signal features, such as internally or externally triggered event-related potentials(ERPs) as well as event-related synchronization and desynchronization, can be used to encode various user commands, and therefore, they are often considered as elementary building blocks for BCI systems. Compared to other types of BCIs, SSVEP based BCIs provide higher information transfer rates with minimal user training, and require fewer EEG channels. Finalized applications should logically be the next challenge for SSVEP-BCI applications. The two key issues in this context are stability and performance; a BCI system is stable if it makes few errors(low error rate), and its performance is good if it provides fast commands(high information transfer rate, ITR).In order to exploit to develop efficient SSVEP-BCI systems, This thesis investigates the crucial issues of SSVEP-BCI research: signal features, feature extraction methods, translation algorithms, output devices, and operational protocols; development of user training strategies; choice of applications and user groups. We optimize four aspects, such as visual stimuli, signal processing, machine learning and cognitive, and we have completed a SSVEP-BCI system applied to 3D image control. Experiments confirmed that the system is practical.