Rapid Protoyping of a Single-Channel Electroencephalogram-Based Brain-Computer Interface
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Date
2006-11-22
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Abstract
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.
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Neural Networks, brain computer interface, EEG
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Degree
MS
Discipline
Biomedical Engineering
