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Decode Brain System: A Dynamic Adaptive Convolutional Quorum Voting Approach for Variable-Length EEG Data

机译：解码脑系统：可变长度EEG数据的动态自适应卷积仲裁法

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Tao Xu; Yun Zhou; Zekai Hou; Wenlan Zhang;
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1. Decode Brain System: A Dynamic Adaptive Convolutional Quorum Voting Approach for Variable-Length EEG Data [J] . Tao Xu, Yun Zhou, Zekai Hou, Complexity . 2020,第1期

机译：解码脑系统：可变长度EEG数据的动态自适应卷积仲裁法
2. Using convolutional neural networks to decode EEG-based functional brain network with different severity of acrophobia [J] . Qiaoxiu Wang, Hong Wang, Fo Hu, Journal of neural engineering . 2021,第1期

机译：使用卷积神经网络解码基于EEG的功能性大脑网络，具有不同的古代的严重性
3. An Approach for Adaptively Approximating the Viterbi Algorithm to Reduce Power Consumption While Decoding Convolutional Codes [J] . Russell Henning, Chaitali Chakrabarti IEEE Transactions on Signal Processing . 2004,第5期

机译：卷积码解码时自适应近似维特比算法以降低功耗的方法
4. Dynamic grid quorum: A novel approach for minimizing power consumption without data migration in grid quorums [C] . Ishikawa M., Hasebe K., Sugiki A., Service-Oriented Computing and Applications (SOCA' 09), 2009 . 2009

机译：动态网格仲裁：一种新颖的方法，可将功耗降至最低，而无需在网格仲裁中进行数据迁移
5. Adaptive Mobile EEG Noise Cancellation Using 2D Convolutional Autoencoders for BCI Authentication [D] . Lewis, Tyree. 2021

机译：适用于使用2D卷积AutoEncoders进行BCI身份验证的自适应移动EEG噪声消除
6. Patient Prognosis from Vital Sign Time Series: Combining Convolutional Neural Networks with a Dynamical Systems Approach [O] . Li-wei Lehman, Mohammad Ghassemi, Jasper Snoek, -1

机译：生命体征时间序列的患者预后：结合卷积神经网络和动态系统方法
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. SIMULATION OF PARALLEL SEQUENTIAL DECODING (Convolution encoder and modified sequential decoder enable communication system to extract reliable information from data transmitted over a noisy link) [R] . J. G. Dodds 1974

机译：并行序列解码的仿真（卷积编码器和改进的顺序解码器使通信系统能够从通过噪声链路传输的数据中提取可靠信息）

Decode Brain System: A Dynamic Adaptive Convolutional Quorum Voting Approach for Variable-Length EEG Data

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