• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

[急性高原低氧对不同情绪状态下脑电图功率的影响]

[Effects of acute high altitude hypoxia on EEG power in different emotional states].

作者信息

Chen Zhen, Zhang Guang-Bo, Zhou Di, Cheng Xiang, Zhu Ling-Ling, Fan Ming, Wang Du-Ming, Zhao Yong-Qi

机构信息

Zhejiang Sci-Tech University, Hangzhou 310018.

Center For Brain Disorders Research, Capotal Medical University and Beijing Institute Of Brain Disorders, Beijing 100069.

出版信息

Zhongguo Ying Yong Sheng Li Xue Za Zhi. 2020 Nov;36(6):556-561. doi: 10.12047/j.cjap.5978.2020.117.

DOI:10.12047/j.cjap.5978.2020.117
PMID:33719257
Abstract

To investigate the effects of acute high altitude hypoxia on EEG power in different emotional states. This study was two-factor within-subject design (2 levels of oxygen environment ×4 levels of emotion type). Twelve male subjects aged between 20 and 25 years old were induced to produce four different types of emotions by emotional picture evoked paradigm: low valence and low arousal(LVLA), high valence and low arousal(HVLA), low valence and high arousal(LVHA), high valence and high arousal(HVHA). Brain Products 32 was used to collect EEG signals under different emotional states. The next day, a constant depressed oxygen chamber was used to simulate a 4 300 m plateau hypoxia environment, and the same group of subjects used the same experimental paradigm to collect EEG signals 10h after hypoxia. The collected EEG signals were analyzed by power spectrum (FFT), and the five frequency bands (Delta, Theta, Alpha, beta, gamma) of the frontal lobe (F3\Fz\F4) were analyzed by variance analysis of two-factor repeated measurements. FFT analysis found that before and after acute hypoxia, the whole brain distribution of alpha wave in four emotional states was mainly concentrated in frontal and parietal leaves; the distribution of alpha wave in the whole brain was the least in relaxed emotional state. The results of the two-factor repeated measurement ANOVA showed that: ①the power of delta\ beta band was significantly affected by the oxygen environment(<0.05), and the power was enhanced under hypoxia. ②The power index of theta\ alpha band showed a significant interaction between the oxygen environment and emotional types(<0.05). Except for the HVLA emotional state, the power of theta alpha band was significantly enhanced under hypoxia. ③ The two factors had no significant influence on the gamma band(>0.05). Under the four kinds of emotional states, the difference of the influence of oxygen environment on brain activity was mainly in the frontal lobe, parietal lobe and part of temporal lobe. Of the four types of emotions, the oxygen environment had the least significant effect on brain activity in HVLA emotional states, while the rest showed significant differences.

摘要

为研究急性高原低氧对不同情绪状态下脑电功率的影响。本研究采用两因素被试内设计(氧环境2水平×情绪类型4水平)。选取12名年龄在20至25岁之间的男性被试,通过情绪图片诱发范式诱导其产生四种不同类型的情绪:低效价低唤醒(LVLA)、高效价低唤醒(HVLA)、低 效价高唤醒(LVHA)、高效价高唤醒(HVHA)。采用Brain Products 32在不同情绪状态下采集脑电信号。次日,使用恒压低氧舱模拟4300米高原低氧环境,同一组被试在低氧10小时后采用相同实验范式采集脑电信号。对采集到的脑电信号进行功率谱(FFT)分析,采用两因素重复测量方差分析对额叶(F3\Fz\F4)的五个频段(δ、θ、α、β、γ)进行分析。FFT分析发现,急性低氧前后,四种情绪状态下α波的全脑分布主要集中在额叶和顶叶;在放松情绪状态下,全脑α波分布最少。两因素重复测量方差分析结果显示:①δ\β频段功率受氧环境影响显著(<0.05),低氧状态下功率增强。②θ\α频段功率指数在氧环境与情绪类型之间存在显著交互作用(<0.05)。除HVLA情绪状态外,低氧状态下θ\α频段功率显著增强。③两因素对γ频段无显著影响(>0.05)。在四种情绪状态下,氧环境对脑活动影响的差异主要集中在额叶、顶叶及部分颞叶。在四种情绪类型中,氧环境对HVLA情绪状态下脑活动的影响最小,其余均表现出显著差异。

相似文献

1
[Effects of acute high altitude hypoxia on EEG power in different emotional states].[急性高原低氧对不同情绪状态下脑电图功率的影响]
Zhongguo Ying Yong Sheng Li Xue Za Zhi. 2020 Nov;36(6):556-561. doi: 10.12047/j.cjap.5978.2020.117.
2
EEG-based emotion classification using LSTM under new paradigm.基于脑电图(EEG)在新范式下使用长短期记忆网络(LSTM)进行情绪分类
Biomed Phys Eng Express. 2021 Sep 27;7(6). doi: 10.1088/2057-1976/ac27c4.
3
Frontal EEG Asymmetry and Middle Line Power Difference in Discrete Emotions.离散情绪中的前额叶脑电图不对称性与中线功率差异
Front Behav Neurosci. 2018 Nov 1;12:225. doi: 10.3389/fnbeh.2018.00225. eCollection 2018.
4
An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals.基于多模态生理信号的情绪图表分析的集成学习方法。
Sensors (Basel). 2022 Dec 4;22(23):9480. doi: 10.3390/s22239480.
5
Assessment of emotional states in EEG signals using multi-frequency power spectrum and functional connectivity patterns.使用多频功率谱和功能连接模式评估 EEG 信号中的情绪状态。
Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:280-283. doi: 10.1109/EMBC48229.2022.9871510.
6
EEG rhythm based emotion recognition using multivariate decomposition and ensemble machine learning classifier.基于 EEG 节律的多变量分解和集成机器学习分类器的情绪识别。
J Neurosci Methods. 2023 Jun 1;393:109879. doi: 10.1016/j.jneumeth.2023.109879. Epub 2023 May 12.
7
Analysis of EEG variables to measure the affective dimensions of arousal and valence related to the vision of emotional pictures.分析脑电图变量以测量与观看情感图片相关的唤醒和效价的情感维度。
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:2518-21. doi: 10.1109/EMBC.2015.7318904.
8
[Emotional Memory and Electrocortical Activity in Schizophrenia].[精神分裂症中的情绪记忆与皮层电活动]
Sante Ment Que. 2016 Spring;41(1):85-121.
9
Low Valence Low Arousal Stimuli: An Effective Candidate for EEG-Based Biometrics Authentication System.低效价低唤醒刺激:基于 EEG 的生物特征认证系统的有效候选者。
Stud Health Technol Inform. 2023 May 18;302:257-261. doi: 10.3233/SHTI230114.
10
Characteristics of EEG activity during high altitude hypoxia and lowland reoxygenation.高原缺氧及平原复氧过程中脑电图活动的特征
Brain Res. 2016 Oct 1;1648(Pt A):243-249. doi: 10.1016/j.brainres.2016.07.013. Epub 2016 Jul 13.

引用本文的文献

1
A systematic review of electroencephalography in acute cerebral hypoxia: clinical and diving implications.急性脑缺氧的脑电图系统评价:临床和潜水意义。
Diving Hyperb Med. 2023 Sep 30;53(3):268-280. doi: 10.28920/dhm53.3.268-280.