The James Bridge: An Ultra-Low-Latency (<11ms) Edge-Quantized Brain-Computer Interface for Real-Time Neural Speech Decoding and Robotic Neurorehabilitation

Abstract

Direct neural interfaces capable of restoring real-time conversational speech and motor autonomy to individuals paralyzed by Amyotrophic Lateral Sclerosis (ALS), brainstem stroke, or Locked-In Syndrome (LIS) have traditionally been constrained by severe algorithmic latency, heavy compute footprints, and reliance on remote server infrastructure. In human sensorimotor loops, processing delays exceeding 20 ms induce cognitive dissonance, disruption of speech prosody, and motor instability. Here, we present The James Bridge, an edge-optimized Brain-Computer Interface (BCI) framework engineered from first principles for sub-11 ms full-pipeline round-trip latency. The architecture integrates a fused JIT-compiled signal processing pipeline (Bandpass, Notch, Common Average Referencing, and Hilbert High-Gamma envelope extraction), modern sequence neural backbones (1D-CNN-LSTM, Conformer, and Diagonal State Space Models), Connectionist Temporal Classification (CTC) prefix beam decoding with n-gram linguistic rescoring, and a deterministic safety-gated robotic actuation engine. Optimized via graph-fused INT8 quantization on embedded edge engines (ONNX Runtime and TensorFlow Lite), the neural decoding model achieves an execution latency of 0.15 ms per frame with an overall pipeline round-trip latency of <11 ms across 128-channel high-density ECoG arrays. Furthermore, the system incorporates a real-time clinical supervision and telemetry subsystem providing electrophysiological anomaly detection, electrode impedance drift monitoring, and cryptographically verifiable audit logging compliant with medical data standards. This work establishes a reproducible, open, and high-velocity foundation for portable neural prostheses.