Low-Latency Neural Inference on an Edge Device for Real-Time Handwriting Recognition from EEG Signals
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918166543728640 |
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| author | Sen, Ovishake Soni, Raghav Virmani, Darpan Parekh, Akshar Lehman, Patrick Jena, Sarthak Katikhaneni, Adithi Khalifa, Adam Chatterjee, Baibhab |
| author_facet | Sen, Ovishake Soni, Raghav Virmani, Darpan Parekh, Akshar Lehman, Patrick Jena, Sarthak Katikhaneni, Adithi Khalifa, Adam Chatterjee, Baibhab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19832 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-Latency Neural Inference on an Edge Device for Real-Time Handwriting Recognition from EEG Signals Sen, Ovishake Soni, Raghav Virmani, Darpan Parekh, Akshar Lehman, Patrick Jena, Sarthak Katikhaneni, Adithi Khalifa, Adam Chatterjee, Baibhab Signal Processing Machine Learning 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. |
| title | Low-Latency Neural Inference on an Edge Device for Real-Time Handwriting Recognition from EEG Signals |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2510.19832 |