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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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A low-power programmable neural spike detection channel with embedded calibration and data compression.

Alberto Rodriguez-Perez¹, Jesús Ruiz-Amaya, Manuel Delgado-Restituto

¹Institute of Microelectronics of Seville, 41092 Sevilla, Spain. alberto@imse-cnm.csic.es

IEEE Transactions on Biomedical Circuits and Systems

|July 16, 2013

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This study presents a programmable neural recording channel for brain-computer interfaces, offering efficient signal tracking and feature extraction with self-calibration. The low-power CMOS design enables advanced neural signal processing for research and clinical applications.

Area of Science:

Neuroscience
Electrical Engineering
Biomedical Engineering

Background:

Advancements in neural recording technologies are crucial for understanding brain function and developing effective treatments for neurological disorders.
Existing neural interfaces often face limitations in power consumption, data processing capabilities, and adaptability to diverse neural signals.

Purpose of the Study:

To develop a highly integrated and programmable neural spike recording channel using standard CMOS technology.
To implement efficient signal acquisition, feature extraction, and self-calibration functionalities within a single channel.
To evaluate the performance of the developed channel in terms of noise, energy efficiency, and power consumption.

Main Methods:

Fabrication of a 400 μm pitch neural recording channel in 130 nm standard CMOS technology.

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Integration of amplification, filtering, digitization, analog spike detection, feature extraction, and self-calibration circuits.

Implementation of two distinct output modes: signal tracking (raw data) and feature extraction (encoded spikes).

Development of a foreground calibration procedure for adjusting amplification gain and filter passband.

Main Results:

Achieved a noise efficiency factor of 2.16 and input-referred noise of 2.84 μVrms.
Demonstrated low energy consumption: 102 fJ/conversion and 14.12 fJ for digitization at 90 kS/s.
Total channel power consumption of 2.8 μW (signal tracking) and 3.1 μW (feature extraction) at 1.2 V.
Successful implementation of programmable gain amplifier and reconfigurable analog-to-digital converter.

Conclusions:

The developed programmable neural recording channel offers a versatile and energy-efficient solution for neural signal acquisition and processing.
The self-calibration feature enhances the reliability and adaptability of the neural interface.
This technology holds promise for next-generation brain-computer interfaces and neural prosthetics.