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Abstract

Introduction:

Advancements in brain–computer interfaces (BCIs) have improved real-time neural signal decoding, enabling adaptive closed-loop neuromodulation. These systems dynamically adjust stimulation parameters based on neural biomarkers, enhancing treatment precision and adaptability. However, existing neuromodulation frameworks often depend on high-power computational platforms, limiting their feasibility for portable, real-time applications.

Methods:

We propose RONDO (Recursive Online Neural DecOding), a resource-efficient neural decoding framework that employs dynamic updating schemes in online learning with recurrent neural networks (RNNs). RONDO supports simple RNNs, long short-term memory networks, and gated recurrent units, allowing flexible adaptation to different signal type, accuracy, and real-time constraints.

Results:

Experimental results show that RONDO’s adaptive model updating improves neural decoding accuracy by 35% to 45% compared to offline learning. Additionally, RONDO operates within real-time constraints of neuroimaging devices without requiring cloud-based or high-performance computing. Its dynamic updating scheme ensures high accuracy with minimal updates, improving energy efficiency and robustness in resource-limited settings.

Conclusions:

RONDO presents a scalable, adaptive, and energy-efficient solution for real-time closed-loop neuromodulation, eliminating reliance on cloud computing. Its flexibility makes it a promising tool for clinical and research applications, advancing personalized neurostimulation and adaptive BCIs.

Impact Statement

This research introduces RONDO (Recursive Online Neural DecOding), a novel framework for real-time, resource-efficient neural decoding in closed-loop neuromodulation and neurofeedback. By leveraging online learning with recurrent neural networks, RONDO enhances adaptability, accuracy, and energy efficiency, addressing key challenges in neurostimulation. Its ability to operate without dependency on cloud computing makes it ideal for portable, real-world applications. This work advances neural decoding technologies, contributing to more precise, personalized treatments for neurological disorders and expanding the potential of adaptive brain–computer interfaces in both clinical and research settings.

Keywords

embedded neuromodulation neural decoding RNN-based neural decoding

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An Online Learning Framework for Neural Decoding in Embedded Neuromodulation Systems