通过深度神经网络实时低延迟估计大脑节律
Ilia Semenkov1,2, Nikita Fedosov1,2, Ilya Makarov1
1Artificial Intelligence Research Institute (AIRI), Moscow 105064, Russia.
Journal of neural engineering
|September 8, 2023
概括
时间卷积网络 (TCN) 为实时大脑-计算机接口提供了一种新的低延迟方法. 这种方法有效地过和预测大脑节律,改善与大脑的交互式通信.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 神经反和脑电脑接口 (BCI) 需要实时解释大脑活动以实现有效的闭环通信.
- 尽量减少神经事件和反之间的延迟对于提高相互作用有效性至关重要.
- 目前用于隔离狭带大脑信号的现有方法带来了根本性的延迟,需要更有效的方法.
研究的目的:
- 探索现代时间序列预测神经网络的实用性,以低延迟提取大脑节律参数.
- 开发和评估新的,高效的方法来跟踪大脑节律阶段和信封即时大脑相互作用.
- 研究神经网络在弥补大脑信号处理延迟方面的潜力.
主要方法:
- 测试了五种不同的神经网络架构,用于预测合成脑电图 (EEG) 节奏.
- 确定了最强的预测架构,并训练它同时过和预测EEG数据.
- 将所选的神经网络的性能与最先进的技术 (cFIR,卡尔曼波器,Conv-TasNet) 进行比较,使用来自25名受试者的合成和真实EEG数据.
主要成果:
- 时间卷积网络 (TCN) 显示出优异的预测性能,实现了>90%的节奏封面相关性,有效延迟<10 ms,阶段估计的循环标准偏差<20°.
- 经过多次测试,TCN表现出对噪声干扰的稳定性.
- 在训练过和预测时,TCN的性能优于cFIR和Kalman过方法,并且与更大的Conv-TasNet架构的性能相匹配.
结论:
- 证明了神经网络方法的有效性,特别是TCNs,用于对大脑活动信号的低延迟窄带过.
- 拟议的基于TCN的框架在各种应用中提高了大脑状态依赖范式的有效性.
- 这种预测框架为研究EEG信号可预测性和理解大脑信息处理提供了有价值的工具.
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