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A closed loop brain computer interface system: Hardware implementation
Shah, Jimit Jaiminkumar
Huang, KeMoon, Kee
Currently available closed-loop brain-computer interface (BCI) systems work as software tools for analysis and stimulation generator for the recorded neural activity in real- time, the main concerns with this software tools are the power consumption, area utilization, and the mobility of the system. The effective hardware implementation of the closed loop BCI system can be used in the many application in real-time neural activity analysis and feedback stimulation generation. The objective of the proposed design is to develop a feedback BCI system which enables the researchers to acquire the brain signal patterns from the 32 channels, record the action potentials. From the recorded action potentials, the system translates the data which gives information about the inter-spike samples and the channel number. This translated data then in-line with monitor screen to probe the visual cortex. The presented work uses various communication protocols such as AXI, TCP/IP, and SPI to interact with different blocks of the design. The prime focus of this thesis is to develop the ARM to FPGA interface and spike detection algorithm which is implemented on the programming logic using Verilog HDL. Along with, generation of the visual stimulation on host computer in real-time. The developed programming logic then incorporated with processing system to achieve the proposed system architecture. The functional performance of the closed loop BCI system is validated using two different set of input data samples. This work can find application in helping the neuroscience researchers to observe neuronal activity in the visual cortex by changing the visual stimulation patterns according to the detected action potential.
Electrical and Computer Engineering
Master of Science (M.S.) San Diego State University, 2017
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