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Summary

This summary is machine-generated.

This study compares 1D and 3D CNN models for decoding neural drive from HD-sEMG signals. 1D CNNs excel with larger windows, while 3D CNNs offer lower latency but higher computational cost.

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Area of Science:

Biomedical Engineering
Neuroscience
Machine Learning

Background:

Convolutional Neural Network (CNN) models, including 1D and 3D CNNs, show promise in decoding neural drive from high-density surface electromyography (HD-sEMG) signals.
The comparative performance of 1D versus 3D CNNs for neural drive decoding using identical datasets remains underexplored.

Purpose of the Study:

To assess and compare the performance of 1D CNN and 3D CNN models in extracting neural drive as a cumulative spike train (CST).
To investigate the influence of critical parameters, specifically window and step sizes, on the decoding accuracy of both CNN dimensionalities.
To evaluate the computational cost associated with 1D and 3D CNN models for neural drive decoding.

Main Methods:

Utilized an experimental HD-sEMG dataset from the gastrocnemius medialis muscle of three participants.
Trained and validated 1D CNN and 3D CNN models to decode neural drive into a cumulative spike train (CST).
Compared model performance using F1 score and correlation coefficient against the convolution kernel compensation (CKC) algorithm across various window and step sizes.

Main Results:

1D CNN achieved peak performance (F1 score: 0.84, correlation: 0.94) with larger window sizes (80-120 samples).
3D CNN reached peak performance (F1 score: 0.83, correlation: 0.92) with smaller window sizes (20-40 samples), suggesting lower latency.
Both models showed performance degradation with increased step sizes, and 3D CNN exhibited significantly higher computational requirements (938G FLOPs vs. 60G FLOPs for 1D CNN).

Conclusions:

Significant differences exist between 1D and 3D CNN architectures for HD-sEMG based neural drive decoding.
Optimal parameter selection (window/step size) is crucial and differs between 1D and 3D CNNs for maximizing accuracy and minimizing latency.
The findings provide guidance for selecting appropriate CNN models and parameters for precise and real-time neural drive decoding applications.