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Hardware Efficient Automatic Thresholding for NEO-Based Neural Spike Detection.

Yuning Yang, Andrew J Mason

IEEE Transactions on Bio-Medical Engineering

|June 21, 2016

Summary

This summary is machine-generated.

This study introduces an automatic thresholding method for nonlinear energy operator (NEO) neural spike detection. The new approach is robust to varying spike firing rates and suitable for neural implants.

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

The nonlinear energy operator (NEO) algorithm is widely used in hardware for neural spike detection.
Traditional threshold setting methods for NEO are sensitive to neural signal firing rates, limiting their effectiveness in real-world applications.

Purpose of the Study:

To develop a novel, real-time, and automatic threshold setting method for NEO spike detection.
To create a thresholding approach that is robust to spike firing rate variations and suitable for neural implants.

Main Methods:

A new threshold calculation method was developed, analyzing neural signal standard deviation and root-mean-square frequency.
The method updates channel thresholds sequentially every few seconds.
Hardware-efficient architectures were designed for estimating statistical parameters required for threshold calculation.

Main Results:

The automatic thresholding method demonstrates robust performance across spike firing rates from 10 to 100 Hz.
The implementation is hardware-efficient, occupying only 0.021 mm² in 130 nm CMOS.
The system exhibits low power consumption, drawing only 50 nW in simulations at a 20-kHz clock speed.

Conclusions:

The proposed automatic thresholding method enhances the reliability of NEO-based neural spike detection.
This rate-robust and hardware-efficient approach is well-suited for integration into neural implants.