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Association between long-range temporal correlations in intrinsic EEG activity and subjective sense of identity

机译：内在EEG活动中远程时间相关性与主观身份感的关联

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Kazumi Sugimura; Yasuhiro Iwasa; Ryota Kobayashi; Tatsuru Honda; Junya Hashimoto; Shiho Kashihara; Jianhong Zhu; Kazuki Yamamoto; Tsuyoshi Kawahara; Mayo Anno; Risa Nakagawa; Kai Hatano; Takashi Nakao;
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1. From neuronal to psychological noise - Long-range temporal correlations in EEG intrinsic activity reduce noise in internally-guided decision making [J] . Nakao Takashi, Miyagi Madoka, Hiramoto Ryosuke, NeuroImage . 2019,第期

机译：从神经元到心理噪声 - 远程噪声的远程时间相关性，在内部活动中降低了内部导向决策的噪声
2. Inference on Long-Range Temporal Correlations in Human EEG Data [J] . Smith Rachel J., Ombao Hernando C., Shrey Daniel W., Biomedical and Health Informatics, IEEE Journal of . 2020,第4期

机译：对人EEG数据中远程时间相关的推断
3. Modeling neuronal avalanches and long-range temporal correlations at the emergence of collective oscillations: Continuously varying exponents mimic M/EEG results [J] . Leonardo Dalla Porta, Mauro Copelli PLoS Computational Biology . 2019,第4期

机译：在集体振荡出现时模拟神经元雪崩和长期时间相关性：连续变化的指数模仿M / EEG结果
4. The long-range temporal correlations of broadband EEG oscillations in poststroke depression subjects with basal ganglia infarction [C] . Annual International Conference of the IEEE Engineering in Medicine and Biology Society . 2016

机译：脑卒中后抑郁症伴基底节梗死的宽脑电图振荡的长期时间相关性
5. An exploration of student worldview formation and integration: Relationships and correlations between religious worldview, personal epistemology, intrinsic motivation to learn and subjective well-being. [D] . Henze, Mark E. 2006

机译：对学生世界观形成和整合的探索：宗教世界观，个人认识论，学习的内在动力和主观幸福感之间的关系和相关性。
6. Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks [O] . Maitreyee Wairagkar, Yoshikatsu Hayashi, Slawomir J. Nasuto 2021

机译：不同电机执行期间宽带脑电图的远程时间相关动态及图像
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）

Association between long-range temporal correlations in intrinsic EEG activity and subjective sense of identity

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