研究脑机接口系统中基于脑电的跨会话和跨任务警戒估计。

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-09-06 DOI:10.1088/1741-2552/acf345
Kangning Wang, Shuang Qiu, Wei Wei, Weibo Yi, Huiguang He, Minpeng Xu, Tzyy-Ping Jung, Dong Ming
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引用次数: 0

摘要

客观的警惕状态对于脑机接口(BCI)任务的有效执行至关重要,因此,研究脑机接口任务中的警惕水平至关重要。尽管如此,大多数研究都集中在驾驶任务中的警惕水平上,而不是脑机接口任务,并且不同脑机接口工作中警惕状态的脑电图(EEG)模式仍不清楚。本研究旨在确定不同脑机接口任务和会话中脑电图模式和警惕性估计性能的异同。方法。为了实现这一点,我们建立了一个基于稳态视觉诱发电位的脑机接口系统和一个基于快速串行视觉呈现的脑机系统,并招募了18名参与者在四天内进行了四次脑机接口实验。主要结果。我们的研究结果表明,高警戒水平和低警戒水平的特定神经模式在整个疗程中相对稳定。差分熵特征在所有频带的不同警戒级别之间以及在德尔塔和θ频带的脑机接口任务之间存在显著差异,θ频带特征在警戒估计中发挥着关键作用。此外,在脑机接口任务中,前额叶、颞叶和枕叶区域与警惕状态更相关。我们的结果表明,跨会话警惕性估计比跨任务估计更准确。意义。我们的研究阐明了两种脑机接口任务中警戒状态的潜在机制,并为进一步研究脑机接口应用中的警戒估计提供了基础。
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Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems.

Objective. The state of vigilance is crucial for effective performance in brain-computer interface (BCI) tasks, and therefore, it is essential to investigate vigilance levels in BCI tasks. Despite this, most studies have focused on vigilance levels in driving tasks rather than on BCI tasks, and the electroencephalogram (EEG) patterns of vigilance states in different BCI tasks remain unclear. This study aimed to identify similarities and differences in EEG patterns and performances of vigilance estimation in different BCI tasks and sessions.Approach.To achieve this, we built a steady-state visual evoked potential-based BCI system and a rapid serial visual presentation-based BCI system and recruited 18 participants to carry out four BCI experimental sessions over four days.Main results. Our findings demonstrate that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy features significantly differ between different vigilance levels in all frequency bands and between BCI tasks in the delta and theta frequency bands, with the theta frequency band features playing a critical role in vigilance estimation. Additionally, prefrontal, temporal, and occipital regions are more relevant to the vigilance state in BCI tasks. Our results suggest that cross-session vigilance estimation is more accurate than cross-task estimation.Significance.Our study clarifies the underlying mechanisms of vigilance state in two BCI tasks and provides a foundation for further research in vigilance estimation in BCI applications.

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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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