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A fast, robust algorithm for power line interference cancellation in neural recording.

Mohammad Reza Keshtkaran¹, Zhi Yang

¹Department of Electrical and Computer Engineering , National University of Singapore, 117583 Singapore.

Journal of Neural Engineering

|March 25, 2014

Summary

This summary is machine-generated.

This study introduces a novel algorithm to effectively remove power line interference from neural recordings. The method ensures high signal quality and fast adaptation for real-time applications.

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Area of Science:

Biomedical Engineering
Signal Processing
Neuroscience

Background:

Neural recordings are often corrupted by power line interference (PLI) at 50/60 Hz and its harmonics.
This interference is non-stationary, exhibiting variations in frequency, amplitude, and phase.
Accurate retrieval of neural signals, particularly gamma-band oscillations, requires effective PLI removal without signal distortion.

Purpose of the Study:

To develop a robust and computationally efficient algorithm for removing power line interference from neural recordings.
To achieve interference cancellation without compromising the integrity of neural signals in the frequency bands of interest.

Main Methods:

An adaptive notch filter estimates the fundamental PLI frequency.
Discrete-time oscillators generate harmonics based on the estimated frequency.
A modified recursive least squares algorithm estimates the amplitude and phase of each harmonic.
The estimated interference is subtracted from the recorded neural data.

Main Results:

The algorithm successfully tracks frequency, phase, and amplitude variations of PLI without requiring a reference signal.
Demonstrates superior performance compared to existing methods in noise immunity, convergence speed, and output signal-to-noise ratio (SNR).
Achieves fast convergence (<100 ms) and substantial interference rejection (output SNR >30 dB) across various interference strengths and frequencies (45-65 Hz).
Features straightforward parameter adjustment, independent of input SNR, signal power, and sampling rate.

Conclusions:

The algorithm offers robust operation, rapid adaptation to interference changes, and significant SNR improvement.
Low computational complexity and memory requirements make it suitable for real-time applications.
Ideal for wearable and implantable sensor systems requiring reliable, real-time interference cancellation.