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

• URL: http://arxiv.org/abs/2510.19832v1
• Date: Tue, 07 Oct 2025 21:20:50 GMT
• Title: Low-Latency Neural Inference on an Edge Device for Real-Time Handwriting Recognition from EEG Signals
• Authors: Ovishake Sen, Raghav Soni, Darpan Virmani, Akshar Parekh, Patrick Lehman, Sarthak Jena, Adithi Katikhaneni, Adam Khalifa, Baibhab Chatterjee,
• Abstract summary: Imagined handwriting provides an intuitive paradigm for character-level neural decoding.<n>Non-invasive electroencephalography (EEG) offers safer and more scalable alternatives but suffers from low signal-to-noise ratio and spatial resolution.<n>This work demonstrates that advanced machine learning combined with informative EEG feature extraction can overcome these barriers.
• Score: 1.0383582379105902
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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