Exploring Day-to-Day Variability in the Relations Between Emotion and EEG Signals

机译：探索情绪和脑电信号之间关系的日常变化

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

Electroencephalography (EEG)-based emotion classification has drawn increasing attention over the last few years and become an emerging direction in brain-computer interfaces (BCI), namely affective BCI (ABCI). Many prior studies devoted to improve emotion-classification models using the data collected within a single session or day. Less attention has been directed to the day-to-day EEG variability associated with emotional responses. This study recorded EEG signals of 12 subjects, each underwent the music-listening experiment on five different days, to assess the day-to-day variability from the perspectives of inter-day data distributions and cross-day emotion classification. The empirical results of this study demonstrated that the clusters of the same emotion across days tended to scatter wider than the clusters of different emotions within a day. Such inter-day variability poses a severe challenge for building an accurate cross-day emotion-classification model in real-life ABCI applications.

机译：在最近几年中，基于脑电图（EEG）的情绪分类吸引了越来越多的关注，并成为脑机接口（BCI）（即情感BCI（ABCI））的新兴方向。许多先前的研究致力于使用单个会话或一天内收集的数据来改进情绪分类模型。与情感反应相关的日常脑电图变异性已被较少关注。这项研究记录了12名受试者的脑电信号，每名受试者在5天不同的时间进行音乐聆听实验，以从日间数据分布和跨日情感分类的角度评估日间的变异性。这项研究的实证结果表明，一天中同一情感的群集比一天中不同情感的群集散布得更大。对于在现实的ABCI应用程序中建立准确的跨日情感分类模型，这种日间变异性提出了严峻的挑战。

著录项

来源
《International conference on human-computer interaction;International conference on augmented cognition》|2015年|461-469|共9页
会议地点
作者
Yuan-Pin Lin; Sheng-Hsiou Hsu; Tzyy-Ping Jung;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类
关键词
EEG-based emotion classification; Day-to-day variability;

机译：基于脑电图的情绪分类;日常变化;

相似文献

外文文献
中文文献
专利

1. Regression Analysis of Coherence between Concurrent EEG-EEG and EEG - CPPG Signals from Prefrontal Cortex During Music Evoked Emotions [J] . Shriram Revati, Baskar V. Vijaya, Martin Betty, Journal of Information Recording . 2019,第3期

机译：音乐诱发情绪时额叶皮质并发脑电与脑电-CPPG信号相干性的回归分析。
2. Exploring Modern Digital Signal Processing Techniques on Physiological Signals in Day-to-Day Life Applications [J] . Balaji M. Sundar Prakash, Jayabharathy R., Martin Betty, Journal of Medical Imaging and Health Informatics . 2020,第1期

机译：探索现代寿命应用中生理信号的现代数字信号处理技术
3. The relation between Scrabble expertise and brain aging as measured with EEG brain signal variability [J] . Hongye Wang, Penny M. Pexman, Gary Turner, Neurobiology of Aging: Experimental and Clinical Research . 2018,第期

机译：用EEG脑信号变异测量的糖椒专业知识和脑老化的关系
4. Exploring Day-to-Day Variability in the Relations Between Emotion and EEG Signals [C] . Yuan-Pin Lin, Sheng-Hsiou Hsu, Tzyy-Ping Jung International Conference on Augmented Cognition . 2015

机译：在情感和脑电图中的关系中探索日常变异性
5. Relations between voluntary emotion regulation, automatic emotion regulation, and frontal EEG asymmetry. [D] . Jackson, Daren C. 2004

机译：自愿情绪调节，自动情绪调节和额叶脑电图不对称之间的关系。
6. REPRODUCIBILITY OF BLOOD PRESSURE DIPPING: RELATION TO DAY-TO-DAY VARIABILITY IN SLEEP QUALITY [O] . Alan L. Hinderliter, Faye S. Routledge, James A. Blumenthal, -1

机译：血压下降的可再现性：与睡眠质量的日常变化有关
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

Exploring Day-to-Day Variability in the Relations Between Emotion and EEG Signals

摘要

著录项

相似文献

相关主题

期刊订阅