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The Muscle Cuff Regenerative Peripheral Nerve Interface for the Amplification of Intact Peripheral Nerve Signals
Published on: January 13, 2022
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A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces
Summary
Machine learning (ML) is increasingly used in peripheral nerve interfaces (PNIs) for neural activity classification. This review found supervised learning, particularly neural networks, dominates PNI applications, suggesting underutilized ML methods could enhance performance.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Machine Learning
Background:
- Peripheral nerve interfaces (PNIs) are crucial for bioelectronic medicine and neuroprosthetics, enabling neural activity modulation and monitoring.
- The integration of Machine Learning (ML) offers advanced capabilities for analyzing complex neural data within PNIs.
- A comprehensive understanding of ML's current role and potential in PNIs is lacking.
Purpose of the Study:
- To conduct a scoping review to map the application and scope of ML techniques in the field of PNIs.
- To identify prevalent ML approaches and document their utilization in PNI research.
- To highlight underutilized ML methodologies for potential future advancements in PNI technology.
Main Methods:
- A systematic search was performed across five major databases to identify relevant studies.
- A total of 63 studies were included after rigorous full-text review.
- The review focused on categorizing and analyzing the ML techniques employed in the selected PNI studies.
Main Results:
- Supervised learning approaches were the most common ML strategy, predominantly used for activity classification in PNIs.
- Neural network algorithms, including artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), were the most frequently applied.
- Unsupervised, semi-supervised, and reinforcement learning (RL) methods are currently underrepresented in PNI research.
Conclusions:
- ML, particularly supervised learning with neural networks, is a key enabler in current PNI applications for neural signal classification.
- There is significant untapped potential for employing other ML paradigms like unsupervised, semi-supervised, and RL to advance PNI capabilities.
- Leveraging underutilized ML techniques could lead to improved performance and novel applications in bioelectronic medicine and neuroprosthetics.
