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: A low-power communication scheme for wireless, 1000 channel brain–machine interfaces
Abstract Objective . Brain–machine interfaces (BMIs) have the potential to restore motor function but are currently limited by electrode count and long-term recording stability. These challenges may be solved through the use of free-floating ‘motes’ which wirelessly transmit recorded neural signals, if power consumption can be kept within safe levels when scaling to thousands of motes. Here, we evaluated a pulse-interval modulation (PIM) communication scheme for infrared (IR)-based motes that aims to reduce the wireless data rate and system power consumption. Approach . To test PIM’s ability to efficiently communicate neural information, we simulated the communication scheme in a real-time closed-loop BMI with non-human primates. Additionally, we performed circuit simulations of an IR-based 1000-mote system to calculate communication accuracy and total power consumption. Main results . We found that PIM at 1 kb/s per channel maintained strong correlations with true firing rate and matched online BMI performance of a traditional wired system. Closed-loop BMI tests suggest that lags as small as 30 ms can have significant performance effects. Finally, unlike other IR communication schemes, PIM is feasible in terms of power, and neural data can accurately be recovered on a receiver using 3 mW for 1000 channels. Significance. These results suggest that PIM-based communication could significantly reduce power usage of wireless motes to enable higher channel-counts for high-performance BMIs.  more » « less
Award ID(s):
1926576
PAR ID:
10351446
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Journal of Neural Engineering
Volume:
19
Issue:
3
ISSN:
1741-2560
Page Range / eLocation ID:
036037
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, 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
  2. ; ; (, IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))
    null (Ed.)
    In this paper, we consider federated learning in wireless edge networks. Transmitting stochastic gradients (SG) or deep model's parameters over a limited-bandwidth wireless channel can incur large training latency and excessive power consumption. Hence, data compressing is often used to reduce the communication overhead. However, efficient communication requires the compression algorithm to satisfy the constraints imposed by the communication medium and take advantage of its characteristics, such as over-the-air computations inherent in wireless multiple-access channels (MAC), unreliable transmission and idle nodes in the edge network, limited transmission power, and preserving the privacy of data. To achieve these goals, we propose a novel framework based on Random Linear Coding (RLC) and develop efficient power management and channel usage techniques to manage the trade-offs between power consumption, communication bit-rate and convergence rate of federated learning over wireless MAC. We show that the proposed encoding/decoding results in an unbiased compression of SG, hence guaranteeing the convergence of the training algorithm without requiring error-feedback. Finally, through simulations, we show the superior performance of the proposed method over other existing techniques. 
    more » « less
  3. ; (, IEEE Custom Integrated Circuits Conference (CICC))
    null (Ed.)
    Adaptive communication for Internet of Things (IoT) and Wireless Body Area Network (WBAN) technologies is becoming increasingly popular due to the large power-performance trade-offs and highly dynamic channel conditions. Path loss, low signal to noise ratio (SNR) in the channel and network congestion adversely affect the data communication, each of which can be taken care of using different strategies such as reducing the data rate (for reducing congestion), increasing the output power (for increased path loss) and application of error correction coding (ECC, for low SNR). In this paper, we present a digital-friendly Transceiver SoC consisting of an RF-DAC based transmitter with orthogonally tunable output power, data rate and ECC that enables optimum system level bit error rate (BER) and energy for over 3-orders of energy-performance scalability, along with an ultra-low-power OOK receiver that receives the transmitter's control bits from a nearby base station for closed-loop control. The data rate and ECC control is achieved through a digital baseband, while a tapped capacitor matching network controls the output power. The energy efficiency of the transmitter is 27.6pJ/b at 10MSps and at 0.8V supply (~9X improvement over state-of-the-art), while the entire SoC (Transmitter+OOK receiver for controller feedback) consumes only 41.5pJ/b. 
    more » « less
  4. ; ; ; ; ; ; (, 2018 International Conference on Computing, Networking and Communications (ICNC))
    In this paper, we investigate the maximum transmission range and power efficiency of Magnetic Induction (MI) subsea wireless communication systems. We propose a new MI channel model based on maximum resonance voltage analysis. We prove that our channel model can maximize the system signal to noise ratio (SNR). Hence, the maximum transmission range of a MI wireless communication system can be determined accordingly. Furthermore, we quantify the eddy current loss of the MI coil antenna in seawater environments. The power consumption of a subsea MI wireless communication system is obtained by summing up the eddy current loss and ohmic loss. Finally, the relationship between the maximum transmission range and power consumption of a subsea MI wireless communication system is determined. 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al (, Journal of Neural Engineering)
    Abstract Objective. Brain–machine interfaces (BMIs) have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user’s own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand. Approach. We delivered a nerve block to the median, radial, and ulnar nerves just proximal to the elbow to simulate finger paralysis, then used a closed-loop BMI to predict finger movements the monkey was attempting to make in two tasks. The BCFES task was one-dimensional in which all fingers moved together, and we used the BMI’s predictions to control FES of the monkey’s finger muscles. The virtual two-finger task was two-dimensional in which the index finger moved simultaneously and independently from the middle, ring, and small fingers, and we used the BMI’s predictions to control movements of virtual fingers, with no FES. Main results. In the BCFES task, the monkey improved his success rate to 83% (1.5 s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5 s median acquisition time, equal to the trial timeout) when attempting to use his temporarily paralyzed hand. In one monkey performing the virtual two-finger task with no FES, we found BMI performance (task success rate and completion time) could be completely recovered following temporary paralysis by executing recalibrated feedback-intention training one time. Significance. These results suggest that BCFES can restore continuous finger function during temporary paralysis using existing low-power technologies and brain-control may not be the limiting factor in a BCFES neuroprosthesis. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email