Brain Prostheses
Deep brain stimulation (DBS) is an effective therapy for numerous neurological disorders, including Parkinson's disease, which affects more than 1.5m people in the US. However, current treatment systems require monthly or weekly adjustments by trained clinicians. Our work demonstrated the first closed-loop system for deep-brain stimulation. A log-based closed-loop Deep Brain Stimulation system detects and processes low-frequency brain field signals to optimize stimulation parameters. The fully self-contained single-chip system incorporates LNAs, a log-ADC, digital log-filters, a log-DSP with a PI-controller, current stimulators, a two-way wireless transceiver, a clock generator, and an energy harvester. The prototype chip consumes 468μW for recording and processing neural signals, stimulation, and two-way wireless communication.
A log-based closed-loop Deep Brain Stimulation system detects and processes low-frequency brain field signals to optimize stimulation parameters. The fully self-contained single-chip system incorporates LNAs, a log-ADC, digital log-filters, a log-DSP with a PI-controller, current stimulators, a two-way wireless transceiver, a clock generator, and an energy harvester. The 2x2mm2 180nm CMOS prototype consumes 468μW for recording and processing neural signals, stimulation, and for two-way wireless communication.
A log-based closed-loop Deep Brain Stimulation system detects and processes low-frequency brain field signals to optimize stimulation parameters. The fully self-contained single-chip system incorporates LNAs, a log-ADC, digital log-filters, a log-DSP with a PI-controller, current stimulators, a two-way wireless transceiver, a clock generator, and an energy harvester. The 2x2mm2 180nm CMOS prototype consumes 468μW for recording and processing neural signals, stimulation, and for two-way wireless communication.
We also study stimulation artifacts, which are a considerable challenge to practical closed-loop stimulation. The challenge is that the recorded signal following a stimulation pulse is corrupted by large stimulation artifacts on recording electrodes, masking underlying neural activity and hindering the closed-loop algorithm's performance. Furthermore, the amplifier's gain must be reduced so that the stimulus does not saturate the amplifier. We have designed and fabricated a neural interfacing integrated circuit for ECoG and LFP signals with a novel adaptive stimulation artifact removal algorithm in real-time to tackle this problem. The algorithm utilizes an adaptive FIR filter, which learns the artifact waveform using a stochastic gradient descent LMS algorithm.
A. E. Mendrela, S.-Y. Park, M. Vöröslakos, M. P. Flynn, E. Yoon, "A Battery-Powered Opto-Electrophysiology Neural Interface with Artifact-Preventing Optical Pulse Shaping," IEEE Symposium on VLSI Circuits, June 2018.
A. E. Mendrela, J. Cho, J. A. Fredenburg, M. P. Flynn, and E. Yoon, "A Bidirectional Neural Interface Circuit With Active Stimulation Artifact Cancellation and Cross-Channel Common-Mode Noise Suppression," IEEE Journal of Solid-State Circuits, April 2016.
Mendrela, A.E. ; Jihyun Cho ; Fredenburg, J.A. ; Chestek, C.A. ; M.P. Flynn; Euisik Yoon, "Enabling closed-loop neural interface: A bi-directional interface circuit with stimulation artifact cancellation and cross-channel CM noise suppression," , Symposium on VLSI Circuits (VLSI Circuits), 2015.
H. Rhew, J. Jeong, J.A. Fredenburg, S. Dodani, P. Patil, M.P. Flynn, “A Wirelessly Powered Log-based Closed-loop Deep Brain Stimulation SoC with RF telemetry for Treatment of Neurological Disorders,” IEEE Symposium on VLSI Circuits, June 2012
J. Lee, H. Rhew, D. R. Kipke and M. P. Flynn, “A 64 Channel Programmable Closed-loop Neurostimulator with 8 Channel Neural Amplifier and Logarithmic ADC,” IEEE Journal of Solid-State Circuits, September 2010.
J. Lee, H. Rhew, D. Kipke and M. P. Flynn, “A 64 Channel Programmable Closed-loop Deep Brain Stimulator with 8 Channel Neural Amplifier and Logarithmic ADC,” IEEE Symposium on VLSI Circuits, June 2008.
A. E. Mendrela, J. Cho, J. A. Fredenburg, M. P. Flynn, and E. Yoon, "A Bidirectional Neural Interface Circuit With Active Stimulation Artifact Cancellation and Cross-Channel Common-Mode Noise Suppression," IEEE Journal of Solid-State Circuits, April 2016.
Mendrela, A.E. ; Jihyun Cho ; Fredenburg, J.A. ; Chestek, C.A. ; M.P. Flynn; Euisik Yoon, "Enabling closed-loop neural interface: A bi-directional interface circuit with stimulation artifact cancellation and cross-channel CM noise suppression," , Symposium on VLSI Circuits (VLSI Circuits), 2015.
H. Rhew, J. Jeong, J.A. Fredenburg, S. Dodani, P. Patil, M.P. Flynn, “A Wirelessly Powered Log-based Closed-loop Deep Brain Stimulation SoC with RF telemetry for Treatment of Neurological Disorders,” IEEE Symposium on VLSI Circuits, June 2012
J. Lee, H. Rhew, D. R. Kipke and M. P. Flynn, “A 64 Channel Programmable Closed-loop Neurostimulator with 8 Channel Neural Amplifier and Logarithmic ADC,” IEEE Journal of Solid-State Circuits, September 2010.
J. Lee, H. Rhew, D. Kipke and M. P. Flynn, “A 64 Channel Programmable Closed-loop Deep Brain Stimulator with 8 Channel Neural Amplifier and Logarithmic ADC,” IEEE Symposium on VLSI Circuits, June 2008.