Real-time low latency estimation of brain rhythms with deep neural networks.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-18 DOI:10.1088/1741-2552/acf7f3
Ilia Semenkov, Nikita Fedosov, Ilya Makarov, Alexei Ossadtchi
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Abstract

Objective.Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increases the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm's phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits.Approach.Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was then trained to simultaneously filter and forecast EEG data. We compared it against the state-of-the-art techniques using synthetic and real data from 25 subjects.Main results.The temporal convolutional network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios>90% rhythm's envelope correlation with<10 ms effective delay and<20∘circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture.Significance.Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.

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利用深度神经网络实时低延迟估计大脑节律。
目的:神经反馈和脑机接口技术为建立与人脑的交互式闭环实时通信开辟了令人兴奋的机会。这需要解释大脑的节律性活动,并及时向大脑产生反馈。神经元事件和适当反馈之间的较低延迟增加了这种相互作用的功效。对于需要与大脑回路即时交互的场景,需要能够跟踪大脑节律的相位和包络的新的更有效的方法。方法。隔离窄带信号会导致基本延迟。在某种程度上,它们可以使用预测模型进行补偿。鉴于现代时间序列预测神经网络的高质量,我们探索了它们在低延迟提取大脑节律参数方面的实用性。我们测试了五种具有概念上不同架构的神经网络,用于预测合成脑电图节律。然后训练最强的架构来同时过滤和预测EEG数据。我们使用来自25名受试者的合成和真实数据将其与最先进的技术进行了比较。主要结果。时间卷积网络(TCN)仍然是在大多数测试场景中实现的最强预测模型,其节律包络相关性>90%,具有显著性。在这里,我们首次证明了神经网络方法在大脑活动信号的低延迟窄带滤波中的实用性。我们提出的方法与有效的实现相结合,增强了大脑状态依赖范式在各种应用中的有效性。此外,我们的脑电信号预测框架有望研究大脑活动的可预测性,为围绕大脑功能组织和层次信息处理特性的基本问题提供有价值的见解。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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