skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: An Efficient Brain-Switch for Asynchronous Brain-Computer Interfaces
Intracortical brain computer interfaces (iBCIs) utilizing extracellular recordings mainly employ in vivo signal processing application-specific integrated circuits (ASICs) to detect action potentials (spikes). Conventionally, “brain-switches” based on spiking activity have been employed to realize asynchronous (self-paced) iBCIs, estimating when the user involves in the underlying BCI task. Several studies have demonstrated that local field potentials (LFPs) can effectively replace action potentials, drastically reducing the power consumption and processing requirements of in vivo ASICs. This article presents the first LFP-based brain-switch design and implementation using gated recurrent neural networks (RNNs). Compared to the previously reported brain-switches, our design requires no exhaustive learning phase for the estimation of optimal recording channels or frequency band selection, making it more applicable to practical asynchronous iBCIs. The synthesized ASIC of the designed in vivo LFP-based feature extraction unit, in a standard 180-nm CMOS process, occupies only 0.09 mm^2 of silicon area, and the post place-and-route synthesis results indicate that it consumes 91.87 nW of power while operating at 2 kHz. Compared to the previously published ASICs, the proposed LFP-based brain-switch consumes the least power for in vivo digital signal processing and achieves comparable state estimation performance to that of spike-based brain-switches.  more » « less
Award ID(s):
2007131
PAR ID:
10528764
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE Transactions on Biomedical Circuits and Systems
Date Published:
Journal Name:
IEEE transactions on biomedical circuits and systems
ISSN:
1932-4545
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, IEEE transactions on biomedical circuits and systems)
    Conventional in vivo neural signal processing involves extracting spiking activity within the recorded signals from an ensemble of neurons and transmitting only spike counts over an adequate interval. However, for brain-computer interface (BCI) applications utilizing continuous local field potentials (LFPs) for cognitive decoding, the volume of neural data to be transmitted to a computer imposes relatively high data rate requirements. This is particularly true for BCIs employing high-density intracortical recordings with hundreds or thousands of electrodes. This article introduces the first autoencoder-based compression digital circuit for the efficient transmission of LFP neural signals. Various algorithmic and architectural-level optimizations are implemented to significantly reduce the computational complexity and memory requirements of the designed in vivo compression circuit. This circuit employs an autoencoder-based neural network, providing a robust signal reconstruction. The application-specific integrated circuit (ASIC) of the in vivo compression logic occupies the smallest silicon area and consumes the lowest power among the reported state-of-the-art compression ASICs. Additionally, it offers a higher compression rate and a superior signal-to-noise and distortion ratio. 
    more » « less
  2. ; ; ; ; (, Journal of Neural Engineering)
    Abstract Objective.Advances in brain–machine interfaces (BMIs) can potentially improve the quality of life of millions of users with spinal cord injury or other neurological disorders by allowing them to interact with the physical environment at their will.Approach.To reduce the power consumption of the brain-implanted interface, this article presents the first hardware realization of anin vivointention-aware interface via brain-state estimation.Main Results.It is shown that incorporating brain-state estimation reduces thein vivopower consumption and reduces total energy dissipation by over 1.8× compared to those of the current systems, enabling longer better life for implanted circuits. The synthesized application-specific integrated circuit (ASIC) of the designed intention-aware multi-unit spike detection system in a standard 180 nm CMOS process occupies 0.03 mm2of silicon area and consumes 0.63 µW of power per channel, which is the least power consumption among the currentin vivoASIC realizations.Significance.The proposed interface is the first practical approach towards realizing asynchronous BMIs while reducing the power consumption of the BMI interface and enhancing neural decoding performance compared to those of the conventional synchronous BMIs. 
    more » « less
  3. ; ; ; ; ; (, IEEE Transactions on Biomedical Circuits and Systems)
    We report a power-efficient analog front-end integrated circuit (IC) for multi-channel, dual-band subcortical recordings. In order to achieve high-resolution multi-channel recordings with low power consumption, we implemented an incremental ΔΣ ADC (IADC) with a dynamic zoom-and-track scheme. This scheme continuously tracks local field potential (LFP) and adaptively adjusts the input dynamic range (DR) into a zoomed sub-LFP range to resolve tiny action potentials. Thanks to the reduced DR, the oversampling rate of the IADC can be reduced by 64.3% compared to the conventional approach, leading to significant power reduction. In addition, dual-band recording can be easily attained because the scheme continuously tracks LFPs without additional on-chip hardware. A prototype four-channel front-end IC has been fabricated in 180 nm standard CMOS processes. The IADC achieved 11.3-bit ENOB at 6.8 μW, resulting in the best Walden and SNDR FoMs, 107.9 fJ/c-s and 162.1 dB, respectively, among two different comparison groups: the IADCs reported up to date in the state-of-the-art neural recording front-ends; and the recent brain recording ADCs using similar zooming or tracking techniques to this work. The intrinsic dual-band recording feature reduces the post-processing FPGA resources for subcortical signal band separation by >45.8%. The front-end IC with the zoom-and-track IADC showed an NEF of 5.9 with input-referred noise of 8.2 μVrms, sufficient for subcortical recording. The performance of the whole front-end IC was successfully validated through in vivo animal experiments. 
    more » « less
  4. ; ; ; ; ; ; ; ; ; (, IEEE Micro)
    Brain-computer interfaces (BCIs) enable direct communication with the brain, providing valuable information about brain function and enabling novel treatment of brain disorders. Our group has been building {\abssys}, a flexible and ultra-low-power processing architecture for BCIs. HALO can process up to 46Mbps of neural data, a significant increase over the interfacing bandwidth achievable by prior BCIs. HALO can also be programmed to support several applications, unlike most prior BCIs. Key to HALO's effectiveness is a hardware accelerator cluster, where each accelerator operates within its own clock domain. A configurable interconnect connects the accelerators to create data flow pipelines that realize neural signal processing algorithms. We have taped out our design in a 12nm CMOS process. The resulting chip runs at 0.88V, per-accelerator frequencies of 3--180MHz, and consumes at most 5.0mW for each signal processing pipeline. Evaluations using electrophysiological data collected from a non-human primate confirm HALO's flexibility and superior performance per watt. 
    more » « less
  5. ; ; ; ; ; (, The Journal of Neuroscience)
    Dopaminergic brain areas are crucial for cognition and their dysregulation is linked to neuropsychiatric disorders typically treated with pharmacological interventions. These treatments often have side effects and variable effectiveness, underscoring the need for alternatives. We introduce the first demonstration of neurofeedback using local field potentials (LFP) from the ventral tegmental area (VTA). This approach leverages the real-time temporal resolution of LFP and ability to target deep brain. In our study, two male rhesus macaque monkeys (Macaca mulatta) learned to regulate VTA beta power using a customized normalized metric to stably quantify VTA LFP signal modulation. The subjects demonstrated flexible and specific control with different strategies for specific frequency bands, revealing new insights into the plasticity of VTA neurons contributing to oscillatory activity that is functionally relevant to many aspects of cognition. Excitingly, the subjects showed transferable patterns, a key criterion for clinical applications beyond training settings. This work provides a foundation for neurofeedback-based treatments, which may be a promising alternative to conventional approaches and open new avenues for understanding and managing neuropsychiatric disorders. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email