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Brain to Brain Communication

Our Brain-to-Brain communication research focuses on advancing the precision of non-invasive recording and stimulation of the brain cortex. The precision is increased by the development of advanced optimization algorithms combined with accurate tissue modeling. The models are augmented by our development of hardware stimulation and recording systems to allow independent fine-tuned stimulation of large amount of channels and fast data transmission between multiple brain-interface devices in real-time. Our bold aim is to step beyond a brain-computer interface and instead make human brain to brain connection possible, thus making scientific fiction a reality.

 

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Stimulation Artifact Cancellation

2016-07-01_15_04_54_02sec-pageLive Stimulation Artifact Cancellation

 

Recovering neural responses from electrode recordings is fundamental for understanding the dynamics of neural networks. This effort is often obscured by stimulus artifacts in the recordings, which result from stimuli injected into the electrode-tissue interface. Stimulus artifacts, which can be orders of magnitude larger than the neural responses of interest, can mask short-latency evoked responses. Furthermore, simultaneous neural stimulation and recording on the same electrode generates artifacts with larger amplitudes compared to a separate electrode setup, which inevitably overwhelm the amplifier operation and cause unrecoverable neural signal loss. This paper proposes an end-to-end system combining hardware and software techniques for actively cancelling stimulus artifacts, avoiding amplifier saturation, and recovering neural responses during current-controlled in-vivo neural stimulation and recording. The proposed system is tested in-vitro under various stimulation settings by stimulating and recording on the same electrode with a superimposed pre-recorded neural signal. Experimental results show that neural responses can be recovered with minimal distortion even during stimulus artifacts that are several orders greater in magnitude.

 

Artifact Cancellation fig1_systemOverview copy
System-Level Overview

 

Publication

“A Hybrid Hardware and Software Approach for Cancelling Stimulus Artifacts During Same-electrode Neural Stimulation and Recording,” Stanislav Culaclii, Brian Kim, Yi-Kai Lo, and Wentai Liu, accepted by EMBC 2016.

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Neural Signal Processing and Telemetry

Recording from a large number of neurons produces vast quantities of data that is highly difficult to extract and interpret due to noise and aggregation of multiple neural signals in a single recording site. In addition to conventional techniques like spike sorting, component analysis, de-noising detection, we are focusing on the cellular and molecular levels to understand the fundamentals of neural signals and behavior of large group of neurons. We found that the traditional deterministic channel models are insufficient for the description of the activities of real neurons. We are currently developing the stochastic kinetic modeling of sodium channel and its validation with the measurement of gate current due to transmembrane protein movements in the order of several picoamps with the bandwidth of several hundred of MHz.

 

Selected Publications

  1. “A 12-channel 6mW Wireless Neural Recording IC with On-the-fly Spike Sorting and UWB Transmitter,” M. Chae, W. Liu, Z. Yang, T. Chen, J. Kim, M. Sivaprakasam, and M. R. Yuce, To appear in International Solid-State Circuits Conference, February 2008.
  2. “An Integrated Multi-Channel Neural Recording System,” M. Chae, W. Liu, and M. Sivaprakasam, BMES Annual Fall Meeting, September 2007.

 

 

Collaborators

  1. Huntington Medical Research Institutes
  2. Arizona State University