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bioRxiv · 10.1101/2023.01.18.524615

A Scalable Framework for Closed-Loop Neuromodulation with Deep Learning

Gebodh, N.·Miskovic, V.·Laszlo, S.·Datta, A.·Bikson, M.
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Abstract

Closed-loop neuromodulation measures dynamic neural or physiological activity to optimize interventions for clinical and nonclinical behavioral, cognitive, wellness, attentional, or general task performance enhancement. Conventional closed-loop stimulation approaches can contain biased biomarker detection (decoders and error-based triggering) and stimulation-type application. We present and verify a novel deep learning framework for designing and deploying flexible, data-driven, automated closed-loop neuromodulation that is scalable using diverse datasets, agnostic to stimulation technology (supporting multi-modal stimulation: tACS, tDCS, tFUS, TMS), and without the need for personalized ground-truth performance data. Our approach is based on identified periods of responsiveness - detected states that result in a change in performance when stimulation is applied compared to no stimulation. To demonstrate our framework, we acquire, analyze, and apply a data-driven approach to our open sourced GX dataset, which includes concurrent physiological (ECG, EOG) and neuronal (EEG) measures, paired with continuous vigilance/attention-fatigue tracking, and High-Definition transcranial electrical stimulation (HD-tES). Our frameworks decision process for intervention application identified 88.26% of trials as correct applications, showed potential improvement with varying stimulation types, or missed opportunities to stimulate, whereas 11.25% of trials were predicted to stimulate at inopportune times. With emerging datasets and stimulation technologies, our unifying and integrative framework; leveraging deep learning (Convolutional Neural Networks - CNNs); demonstrates the adaptability and feasibility of automated multimodal neuromodulation for both clinical and nonclinical applications.

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Gebodh, N., Miskovic, V., Laszlo, S., Datta, A., Bikson, M.. 2023-01-20. A Scalable Framework for Closed-Loop Neuromodulation with Deep Learning. https://doi.org/10.1101/2023.01.18.524615

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