A Real-Time GUI Platform for Neural Signal Transmission: Design, Resource Profiling, and Usability

Abstract

Neural decoding systems often achieve strong offline performance, yet many fail to translate that performance into reliable real-time operation due to fragmented pipelines, limited reproducibility, and insufficient visibility into computational costs. This paper presents a real-time graphical user interface (GUI) platform for neural signal transmission that integrates preprocessing, optional feature extraction, model inference, visualization, and continuous resource monitoring within a single operational workflow. The platform supports three execution modes: ESN-only, LSTM-only, and a hybrid ESN LSTM pipeline, enabling systematic comparison of accuracy, latency, and hardware footprint under consistent conditions. Evaluation is conducted using the PhysioNet EEGMMIDB v1.0.0 (motor imagery EEG from nine subjects), with configurable sampling rates and channel selection to reflect practical deployment constraints. To ensure deterministic behavior, the platform incorporates controlled random seeding and structured logging of model and runtime configurations, improving repeatability across sessions and devices. The system also reports per-stage latency, CPU/RAM utilization, and profiling overhead to help practitioners identify bottlenecks and quantify trade-offs. Experimental observations indicate reduced blocking delays and minimal monitoring overhead, supporting stable long-session operation. Overall, the proposed platform bridges the gap between algorithmic neural decoding research and deployable, testable real-time systems by combining reproducible modeling, multi-mode execution, and transparent performance profiling in an integrated GUI environment. The primary contribution is an engineering and reproducibility framework rather than a new learning algorithm‚ as the decoding models used are well established independent of these papers․ The novelty lies in the deterministic‚ fully profiled assembly of the models into a single deployable real-time setting․ To this end‚ decoding performance is given as a secondary but fully detailed metric (per-subject‚ with confidence intervals)‚ along with system-level latency‚ resource requirements‚ and stability measures․