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

Brain–computer interfaces (BCIs) hold significant promise for restoring communication in individuals with severe motor or speech impairments. Imagined handwriting, as a form of motor imagery, offers an intuitive paradigm for character-level neural decoding. While invasive techniques such as electrocorticography (ECoG) offer high decoding accuracy, their surgical requirements pose clinical risks and hinder scalability. Non-invasive alternatives like electroencephalography (EEG) are safer and more accessible but suffer from low signal-to-noise ratio (SNR) and spatial resolution, limiting their effectiveness in high-resolution decoding. Here, we investigate how advanced machine learning, combined with informative feature extraction, can overcome these limitations—enabling EEG-based decoding performance that approaches invasive methods, while supporting real-time inference on edge devices. We present the first real-time, low-latency, high-accuracy system for decoding imagined handwriting from non-invasive EEG signals on a portable edge device. EEG data were collected from seven participants using a 32-channel headcap and preprocessed with bandpass filtering and artifact subspace reconstruction. We extracted 20 time-and frequency-domain features, then applied Pearson correlation coefficient-based feature selection to reduce latency while preserving accuracy. A hybrid architecture combining a Temporal Convolutional Network (TCN) and a multilayer perceptron(MLP) was trained on the extracted features and deployed on the NVIDIA Jetson TX2. The system achieved 83.64%±0.50%accuracy with 766.68 ms per-character inference latency. By selecting only four key features, the model incurred a minimal accuracy loss of less than 1%, while achieving a 4.93× reduction in inference latency (155.68 ms) compared to the full 20-feature set. These findings show that non-invasive EEG, combined with efficient feature and model design, can enable accurate, real-time neural decoding on low-power edge devices—paving the way for practical, portable BCIs.