{"ID":2859606,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19832","arxiv_id":"2510.19832","title":"Low-Latency Neural Inference on an Edge Device for Real-Time Handwriting Recognition from EEG Signals","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.","short_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 a...","url_abs":"https://arxiv.org/abs/2510.19832","url_pdf":"https://arxiv.org/pdf/2510.19832v1","authors":"[\"Ovishake Sen\",\"Raghav Soni\",\"Darpan Virmani\",\"Akshar Parekh\",\"Patrick Lehman\",\"Sarthak Jena\",\"Adithi Katikhaneni\",\"Adam Khalifa\",\"Baibhab Chatterjee\"]","published":"2025-10-07T21:20:50Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\"]","methods":"[]","has_code":false}