Acquisition technology research of EEG and related physiological signals under +Gz acceleration.

Y Li; T Zhang; L Deng; B Wang

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Acquisition technology research of EEG and related physiological signals under +Gz acceleration.

机译：+ Gz加速度下脑电信号及相关生理信号的采集技术研究。

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摘要

The proposed methods establish the basis and have an important guiding significance for follow-up experiment development, EEG features spectral analysis and correlation research of all signals.

机译：所提出的方法为后续实验的发展，脑电图的频谱分析和所有信号的相关性研究奠定了基础，并具有重要的指导意义。

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《Irish journal of medical science》 |2014年第2期|共11页
作者
Y Li; T Zhang; L Deng; B Wang;
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正文语种 eng
中图分类医药、卫生;
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1. Acquisition technology research of EEG and related physiological signals under +Gz acceleration. [J] . Y Li, T Zhang, L Deng, Irish journal of medical science . 2014,第2期

机译：+ Gz加速度下脑电信号及相关生理信号的采集技术研究。
2. Low-Power High-Input-Impedance EEG Signal Acquisition SoC With Fully Integrated IA and Signal-Specific ADC for Wearable Applications [J] . Tohidi Mohammad, Madsen Jens Kargaard, Moradi Farshad IEEE transactions on biomedical circuits and systems . 2019,第6期

机译：低功耗高输入阻抗EEG信号采集SOC，具有完全集成的IA和特定于可穿戴应用的ADC
3. A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals [J] . M. Abo-Zahhad, Sabah M. Ahmed, Sherif N. Abbas International Journal of Intelligent Systems and Applications . 2015,第6期

机译：一种新的脑电信号采集协议，用于使用眨眼信号进行生物特征识别
4. EEG and related physiological signals investigation under the condition of +Gz accelerations [C] . Li Yifeng, Zhang Tao, Wang Bei, 2012 ICME International Conference on Complex Medical Engineering . 2012

机译：+ Gz加速度条件下的脑电图及相关生理信号研究
5. Essays on Information Technology and Value Creation: Corporate Governance, Software Firm Acquisitions, and Entrepreneurial Signaling. [D] . Bandodkar, Nikhil Ramkrishna. 2017

机译：有关信息技术和价值创造的论文：公司治理，软件公司收购和创业信号。
6. Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients [O] . Xiaokang Shu, Shugeng Chen, Lin Yao, 2018

机译：利用脑电信号的生理特征快速识别BCI低效用户：中风患者的筛查研究
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）
8. Electromyographic Signals of the Spinal Musculature during (+)Gz Impact Acceleration. [R] . Tennyson, S. A., Mital, N. K., King, A. I. 1976

机译：在（+）Gz冲击加速期间脊柱肌肉组织的肌电信号。

Acquisition technology research of EEG and related physiological signals under +Gz acceleration.

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