Caffeine on the mind: EEG and cardiovascular signatures of cortical arousal revealed by wearable sensors and machine learning-a pilot study on a male group.

IF 3.5 4区 医学 Q2 NEUROSCIENCES
Frontiers in Systems Neuroscience Pub Date : 2025-09-15 eCollection Date: 2025-01-01 DOI:10.3389/fnsys.2025.1611293
Shabbir Chowdhury, Ahmed Munis Alanazi, Eyad Talal Attar
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引用次数: 0

Abstract

Introduction: Caffeine is the most widely consumed psychoactive substance, and its stimulant properties are well documented, but few investigations have examined its acute effects on brain and cardiovascular responses during cognitively demanding tasks under ecologically valid conditions.

Method: This study used wearable biosensors and machine learning analysis to evaluate the effects of moderate caffeine (162 mg) on heart rate variability (HRV), entropy, pulse transit time (PTT), blood pressure, and EEG activity. Twelve healthy male participants (20-30 years) completed a within-subjects protocol with pre-caffeine and post-caffeine sessions. EEG was recorded from seven central electrodes (C3, Cz, C4, CP1, CP2, CP5, CP6) using the EMOTIV EPOC Flex system, and heart rate (HR) and blood pressure (BP) were continuously monitored via the Huawei Watch D. Data analysis included power spectral density (PSD) estimation, FOOOF decomposition, and unsupervised k-means clustering.

Results: Paired-sample t-tests assessed physiological and EEG changes. Although systolic and diastolic BP showed a non-significant upward trend, HR decreased significantly after caffeine intake (77 ± 5.3 bpm to 72 ± 2.5 bpm, p = 0.027). There was a significant increase in absolute alpha power suppression (from -5.1 ± 0.8 dB to -6.9 ± 0.9 dB, p = 0.04) and beta power enhancement (-4.7 ± 1.2 dB to -2.3 ± 1/1, p = 0.04). The surface data from FOOOF shows these are real oscillatory changes. Based on the changes in clustering prior and post-caffeine, a machine-learning change in the brain activity differentiated pre/post-caffeine states with unsupervised clustering. The study results show that moderate caffeine resulted in synchronized EEG and cardiovascular changes, indicating increased arousal and cortical activation that are detectable with wearable biosensors and classifiable with machine learning.

Conclusion: A fully integrated, non-invasive methodology based on a wearable device for real-time monitoring of cognitive states holds promise in the context of digital health, fatigue detection, and public health awareness efforts.

咖啡因对大脑的影响:可穿戴传感器和机器学习揭示的大脑皮层觉醒的脑电图和心血管特征——一项针对男性群体的初步研究。
简介:咖啡因是最广泛使用的精神活性物质,其兴奋特性已被充分记录,但很少有研究检查其在生态有效条件下认知要求高的任务中对大脑和心血管反应的急性影响。方法:本研究采用可穿戴生物传感器和机器学习分析技术,评估适量咖啡因(162 mg)对心率变异性(HRV)、熵、脉冲传递时间(PTT)、血压和脑电图活动的影响。12名健康男性参与者(20-30岁 )完成了咖啡因前和咖啡因后的受试者协议。采用EMOTIV EPOC Flex系统从7个中心电极(C3、Cz、C4、CP1、CP2、CP5、CP6)记录脑电图,通过Huawei Watch d连续监测心率(HR)和血压(BP),数据分析包括功率谱密度(PSD)估计、FOOOF分解和无监督k-means聚类。结果:配对样本t检验评估生理和脑电图变化。虽然收缩压和舒张压呈不明显上升趋势,但咖啡因摄入后HR明显下降(77 ± 5.3 bpm至72 ± 2.5 bpm, p = 0.027)。绝对alpha权力抑制有显著增加(从-5.1 ±0.8  dB -6.9 ±0.9  dB, p = 0.04)和β力量增强( -4.7±1.2  dB -2.3 ± 1/1,p = 0.04)。来自FOOOF的地面数据显示,这些都是真实的振荡变化。基于咖啡因前和咖啡因后的聚类变化,机器学习的大脑活动变化区分了咖啡因前和咖啡因后的无监督聚类状态。研究结果表明,适量咖啡因会导致脑电图和心血管同步变化,表明可穿戴生物传感器可检测到的觉醒和皮层激活增加,并可通过机器学习进行分类。结论:基于可穿戴设备的认知状态实时监测的完全集成、非侵入性方法在数字健康、疲劳检测和公共卫生意识工作的背景下具有前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Systems Neuroscience
Frontiers in Systems Neuroscience Neuroscience-Developmental Neuroscience
CiteScore
6.00
自引率
3.30%
发文量
144
审稿时长
14 weeks
期刊介绍: Frontiers in Systems Neuroscience publishes rigorously peer-reviewed research that advances our understanding of whole systems of the brain, including those involved in sensation, movement, learning and memory, attention, reward, decision-making, reasoning, executive functions, and emotions.
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