# Low-Complexity CNN-Based Classification of Electroneurographic Signals

arXiv: https://arxiv.org/abs/2505.06241 · HTML: https://mlsanity.com/paper/2505.06241/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 27 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Artificial Neural Networks-based Real-time Classification of ENG Signals for Implanted Nerve Interfaces](https://mlsanity.com/paper/2403.20234/) (2024)

## Abstract (arXiv)

Peripheral nerve interfaces (PNIs) facilitate neural recording and stimulation for treating nerve injuries, but real-time classification of electroneurographic (ENG) signals remains challenging due to constraints on complexity and latency, particularly in implantable devices. This study introduces MobilESCAPE-Net, a lightweight architecture that reduces computational cost while maintaining and slightly improving classification performance. Compared to the state-of-the-art ESCAPE-Net, MobilESCAPE-Net achieves comparable accuracy and F1-score with significantly lower complexity, reducing trainable parameters by 99.9% and floating point operations per second by 92.47%, enabling faster inference and real-time processing. Its efficiency makes it well-suited for low-complexity ENG signal classification in resource-constrained environments such as implantable devices.