Detection and Reconstruction of Poor-Quality Channels in High-Density EMG Array Measurements.

Area of Science:

• Biomedical Engineering
• Signal Processing
• Neuroscience

Background:

• High-density electromyography (HD-EMG) arrays capture muscle electrical activity in spatio-temporal dimensions.
• HD-EMG data often suffers from noise and artifacts, leading to poor-quality channels that compromise analysis.
• Accurate signal processing is crucial for reliable interpretation of muscle function from HD-EMG.

Purpose of the Study:

• To develop and validate an interpolation-based method for detecting and reconstructing poor-quality channels in HD-EMG data.
• To compare the performance of the proposed method against existing techniques like RMS and NMI.
• To assess the effectiveness of the method in both simulated and real HD-EMG datasets.

Main Methods:

• An interpolation-based algorithm was developed to identify channels with poor signal quality in HD-EMG arrays.
• The method's detection performance was evaluated using precision, recall, and F1 scores at various signal-to-noise ratio (SNR) levels.
• 2D spline interpolation was employed to reconstruct the signal in the identified poor-quality channels.

Main Results:

• The interpolation-based method achieved high precision (≥99.9%) and recall (≥97.6%) for detecting channels with SNR 0 dB and lower.
• It significantly outperformed RMS and NMI methods, demonstrating superior F1 scores (99.1% vs. 39.7% and 75.9% at 0 dB SNR).
• Reconstruction of faulty channels using 2D spline interpolation resulted in a percent residual difference (PRD) of 15.5 ± 12.1%.

Conclusions:

• The proposed interpolation-based method is highly effective for detecting and reconstructing poor-quality channels in HD-EMG.
• This technique offers a robust solution for improving the reliability and accuracy of HD-EMG signal analysis.
• The localized contextual evaluation of channel quality distinguishes this method from others.