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A Real Time Closed Loop Brain Computer Interface System

机译:实时闭环脑计算机接口系统

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摘要

Real Time Closed Loop Brain Computer Interfaces allow recording of neural signals via electrodes implanted on the surface of the brain or inner cortical issue. The main aim of this thesis is to probe the brain activity by generating a visual stimulation. The real time Brain Computer Interfaces records data from 32 channels, performs real time spike detection to perform a visual stimulation based on the spike activity recorded from the brain. The requirement of the closed loop BCI system is to record data at the high sampling rate, and perform high speed filtering on each channel to remove noise, detect the spikes simultaneously on all channels, and drive the visual stimuli based on the spike activity detected.;The focus of this study is divided into two parts: The first focus of this thesis is to develop a system which can record the brain signals at a rate of 10kSamples/s thus acquiring the actions of high frequency spikes. This study uses ZYNQ7000 chip which consist of ARM core and FPGA core. ARM core is used to fetch data from sensor and FPGA core is used for performing real time spike detection on 32 channels.;The second focus of this study is to generate a visual stimulation based on the spike activity which acts as a feedback to the brain and completes the closed loop BCI system. This would allow us to generate controlled visual stimuli based on the spike information rather than generating random visual patterns.
机译:实时闭环大脑计算机接口允许通过植入在大脑表面或内皮层问题上的电极记录神经信号。本文的主要目的是通过产生视觉刺激来探测大脑活动。实时大脑计算机接口记录来自32个通道的数据,基于从大脑记录的尖峰活动,执行实时尖峰检测以执行视觉刺激。闭环BCI系统的要求是以高采样率记录数据,并在每个通道上执行高速滤波以消除噪声,同时在所有通道上检测尖峰,并根据检测到的尖峰活动驱动视觉刺激。 ;本研究的重点分为两个部分:本论文的第一个重点是开发一个可以以10kSamples / s的速度记录大脑信号从而获得高频尖峰动作的系统。本研究使用ZYNQ7000芯片,该芯片由ARM内核和FPGA内核组成。 ARM内核用于从传感器获取数据,FPGA内核用于在32个通道上执行实时峰值检测;该研究的第二个重点是基于峰值活动生成视觉刺激,以对大脑进行反馈并完成闭环BCI系统。这将使我们能够基于尖峰信息生成受控的视觉刺激,而不是生成随机的视觉模式。

著录项

  • 作者

    Damani, Darpan Dinesh.;

  • 作者单位

    San Diego State University.;

  • 授予单位 San Diego State University.;
  • 学科 Electrical engineering.
  • 学位 M.S.
  • 年度 2018
  • 页码 64 p.
  • 总页数 64
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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