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Updated: Jun 17, 2026

A Wireless, Bidirectional Interface for In Vivo Recording and Stimulation of Neural Activity in Freely Behaving Rats

A Wireless, Bidirectional Interface for In Vivo Recording and Stimulation of Neural Activity in Freely Behaving Rats

Published on: November 7, 2017

Embedded neural recording with TinyOS-based wireless-enabled processor modules.

Shahin Farshchi¹, Aleksey Pesterev, Paul Nuyujukian

¹Lux Capital Management, New York, NY 10017, USA. shahin@cal.berkeley.edu

IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society

|January 15, 2010

Summary

This summary is machine-generated.

A new wireless neural recording system leverages advanced embedded systems for high-fidelity data acquisition. This system enables remote control and real-time monitoring of neural signals, advancing neuroscience research capabilities.

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Area of Science:

Biomedical Engineering
Neuroscience Technology
Embedded Systems

Background:

Advancements in embedded microcontrollers and communication technologies enable new possibilities for neural recording.
Existing wireless neural recording systems can be improved with integrated, high-performance components.

Purpose of the Study:

To design, fabricate, and test an embedded-system-based architecture for wireless neural recording.
To develop a system capable of acquiring and transmitting neural signals with high fidelity and remote controllability.

Main Methods:

Utilized commercial-off-the-shelf wireless-enabled processor modules (motes).
Developed a custom low-noise neural-signal amplifier fabricated in a 1.5-micrometer CMOS process.
Created a neural-signal-acquisition application for data capture and transmission.

Main Results:

The system acquires neural signals at 4000 12-bit samples/second or detects spikes at 16670 12-bit samples/second.
A custom amplifier with adjustable gain and high-pass corner frequency was successfully integrated.
A client application allows remote control of operation modes and amplifier settings.

Conclusions:

The designed embedded-system architecture provides a robust platform for wireless neural recording.
The system offers high-resolution data acquisition and flexible remote control for neuroscience applications.
This technology benefits from current embedded systems advancements for improved neural signal analysis.