# Low-Latency Neural Inference on an Edge Device for Real-Time Handwriting Recognition from EEG Signals

arXiv: https://arxiv.org/abs/2510.19832 · HTML: https://mlsanity.com/paper/2510.19832/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces](https://mlsanity.com/paper/2006.00622/) (2020)
- [MLPerf Mobile Inference Benchmark](https://mlsanity.com/paper/2012.02328/) (2020)
- [Deep Representation Learning for Open Vocabulary Electroencephalography-to-Text Decoding](https://mlsanity.com/paper/2312.09430/) (2023)

## Built on by (most cited first, 1 of 1)

- [RETRACE: Resilience-Guided Trait-Conditioned Craving Estimation from Wearable Physiology in Opioid Use Disorder](https://mlsanity.com/paper/2608.14947/) (2026)

## Abstract (arXiv)

Brain-computer interfaces (BCIs) offer a pathway to restore communication for individuals with severe motor or speech impairments. Imagined handwriting provides an intuitive paradigm for character-level neural decoding, bridging the gap between human intention and digital communication. While invasive approaches such as electrocorticography (ECoG) achieve high accuracy, their surgical risks limit widespread adoption. Non-invasive electroencephalography (EEG) offers safer and more scalable alternatives but suffers from low signal-to-noise ratio and spatial resolution, constraining its decoding precision. This work demonstrates that advanced machine learning combined with informative EEG feature extraction can overcome these barriers, enabling real-time, high-accuracy neural decoding on portable edge devices. A 32-channel EEG dataset was collected from fifteen participants performing imagined handwriting. Signals were preprocessed with bandpass filtering and artifact subspace reconstruction, followed by extraction of 85 time-, frequency-, and graphical-domain features. A hybrid architecture, EEdGeNet, integrates a Temporal Convolutional Network with a multilayer perceptron trained on the extracted features. When deployed on an NVIDIA Jetson TX2, the system achieved 89.83 percent accuracy with 914.18 ms per-character latency. Selecting only ten key features reduced latency by 4.5 times to 202.6 ms with less than 1 percent loss in accuracy. These results establish a pathway for accurate, low-latency, and fully portable non-invasive BCIs supporting real-time communication.