The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation

Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely. It is a promising new method...

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Bibliographic Details
Published in:PloS one 2022-08, Vol.17 (8), p.e0270696-e0270696
Main Authors: Valenchon, Nicolas, Bouteiller, Yann, Jourde, Hugo R, L'Heureux, Xavier, Sobral, Milo, Coffey, Emily B. J, Beltrame, Giovanni
Format: Article
Language:English
Subjects:
Activity patterns
Algorithms
Analysis
Annotations
Biology and Life Sciences
Brain
Brain research
Brain stimulation
Closed loop systems
Computer and Information Sciences
Deep learning
EEG
Electrical stimuli
Electroencephalography
Engineering and Technology
Evaluation
Flexibility
Machine learning
Medicine and Health Sciences
Memory
Nervous system
Neural networks
Neurosciences
Oscillations
Physiology
Real time
Research and Analysis Methods
Science
Sleep
Stimulation
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Summary:Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely. It is a promising new method for fundamental neuroscience and perhaps for clinical applications such as restoring degraded memory function; however, existing tools are expensive, cumbersome, and offer limited experimental flexibility. In this article, we propose the Portiloop, a deep learning-based, portable and low-cost closed-loop stimulation system able to target specific brain oscillations. We first document open-hardware implementations that can be constructed from commercially available components. We also provide a fast, lightweight neural network model and an exploration algorithm that automatically optimizes the model hyperparameters to the desired brain oscillation. Finally, we validate the technology on a challenging test case of real-time sleep spindle detection, with results comparable to off-line expert performance on the Massive Online Data Annotation spindle dataset (MODA; group consensus). Software and plans are available to the community as an open science initiative to encourage further development and advance closed-loop neuroscience research [
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0270696