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.
翻译:闭环脑刺激是指捕捉神经生理学措施,如电子脑物理学(EEG),迅速发现令人感兴趣的神经事件,并产生听力、磁力或电动刺激,以便与大脑过程进行精确互动。这是基本神经科学和临床应用(如恢复退化的记忆功能)的一种有希望的新方法;然而,现有工具昂贵、繁琐,并提供了有限的实验灵活性。在本篇文章中,我们提议Portilooop是一个深层次的基于学习的、便携式和低成本的闭环刺激系统,能够针对特定的大脑振荡。我们首先用文件记录可以从商业可用的部件中构建的开放硬件实施。我们还提供了快速、轻量的神经网络模型和探索算法,自动优化模型超参数以适应理想的大脑振荡功能。最后,我们验证了实时睡眠螺旋检测这一具有挑战性的测试案例的技术,其结果与大规模在线数据脊柱数据集(MID;集体共识)的离线专家性表现相类似。软件和计划可供社区使用,作为开放科学举措,鼓励进一步开发和封闭的神经科学研究。