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通过可穿戴监测进行听觉、味觉和嗅觉刺激调节大脑认知状态。

Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring.

机构信息

Electrical and Computer Engineering Department, University of Houston, Houston, TX, 77004, USA.

Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, 94143, USA.

出版信息

Sci Rep. 2023 Aug 8;13(1):12399. doi: 10.1038/s41598-023-37829-z.

Abstract

Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants' brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life.

摘要

受可穿戴技术进步的启发,我们设计并进行了人体实验。我们旨在研究应用安全激励(即听觉、味觉和嗅觉)来调节认知唤醒和增强表现状态的效果。在两个拟议的实验中,要求受试者执行一项称为 n-back 任务的工作记忆实验。接下来,我们将听不同类型的音乐、喝咖啡和闻香水作为安全激励器。我们采用信号处理方法来无缝推断参与者的大脑认知状态。结果表明,所提出的安全激励在调节唤醒状态和提高表现水平方面是有效的。仅使用可穿戴设备进行人体监测和使用安全激励干预是拟议实验的关键组成部分。我们的数据集填补了使用可穿戴设备和安全日常激励器对内部大脑状态进行自我管理的公共可用数据集缺乏的空白。该数据集可促进进一步的机器学习和系统识别研究,以促进未来的智能工作环境。这将使我们最终能够开发实用的自动化个性化闭环架构,以管理内部大脑状态并提高生活质量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fafd/10409795/3a95eb799b5b/41598_2023_37829_Fig1_HTML.jpg

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