Rapid Protoyping of a Single-Channel Electroencephalogram-Based Brain-Computer Interface

Neural Networks;brain computer interface;EEG

Adcock, David Brooks, Jr ; John Muth, Committee Member,Lianne Cartee, Committee Co-Chair,Edward Grant, Committee Chair,Adcock, David Brooks, Jr ; John Muth ; Committee Member ; Lianne Cartee ; Committee Co-Chair ; Edward Grant ; Committee Chair

University:North Carolina State University

关键词: Neural Networks;    brain computer interface;    EEG;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/2532/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

This work describes the design, construction and implementation of a single-channel, electroencephalogram-based (EEG) brain-computer interface (BCI) for the prediction of a single-degree-of-freedom kinematic variable. The system employs a custom-built EEG amplifier to increase noise rejection and decrease the overall cost of the BCI. The EEG amplifier output is read into Matlab synchronously with an analog elbow-angle measurement taken from the test subject's left arm. Sampling is done at 300Hz using a 12-bit National Instruments PCI-6025E data acquisition card. Data is software filtered, processed, and logged in Matlab in real-time on a standard PC. At the end of an initial data acquisition period, a feed-forward backpropagation artificial neural network (ANN) is briefly trained off-line to predict subject elbow angle based solely on recorded EEG activity. Upon resuming recording, the system is accurately able to predict the test subject's elbow angle in real-time. If employed in a robotic system, this BCI would have applications in rehabilitation robotics, search and rescue, tele-robotics and exoskeleton research.