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On the time-course and frequency selectivity of the EEG for different modes of response selection: Evidence from speech production and keyboard pressing

机译：关于不同响应选择的脑电图的时机和频率选择性：来自语音生产和键盘压制的证据

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Pascale Tremblay; Douglas M. Shiller; Vincent L. Gracco;
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1. On the time-course and frequency selectivity of the EEG for different modes of response selection: Evidence from speech production and keyboard pressing [J] . Pascale Tremblay Clinical neurophysiology . 2008,第1期

机译：关于脑电图在不同响应选择模式下的时程和频率选择性：来自语音产生和键盘按下的证据
2. Left Fronto-posterior Network Underlying Lexical Selection in Speech Production: Neuropsychological and EEG Evidence [J] . S. Ries, N. Dronkers, R. Knight Procedia - Social and Behavioral Sciences . 2012,第2期

机译：语音产生中词汇选择基础的左前-后网络：神经心理学和脑电证据
3. The Right Temporoparietal Junction Supports Speech Tracking During Selective Listening: Evidence from Concurrent EEG-fMRI [J] . Puschmann Sebastian, Steinkamp Simon, Gillich Imke, The Journal of Neuroscience: The Official Journal of the Society for Neuroscience . 2017,第47期

机译：正确的临时交界处支持在选择性听力期间的语音跟踪：来自同时EEG-FMRI的证据
4. Time-Course EEG Spectrum Evidence for Music Key Perception and Emotional Effects [C] . Hongjian Bo, Haifeng Li, Lin Ma, International conference on brain-inspired cognitive systems . 2016

机译：音乐键感知和情感效果的时间课程EEG频谱证据
5. Signal Models for Fusion of Multiple Brain Responses and Context Evidence Sources in EEG-Based BCIS [D] . Gonzalez-Navarro, Paula 2018

机译：基于EEG的BCIS中多种脑反应和情境证据源融合的信号模型
6. The Right Temporoparietal Junction Supports Speech Tracking During Selective Listening: Evidence from Concurrent EEG-fMRI [O] . Sebastian Puschmann, Simon Steinkamp, Imke Gillich, 2017

机译：正确的颞颞交界处支持选择性听力期间的语音跟踪：同时进行的EEG-fMRI的证据
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）

On the time-course and frequency selectivity of the EEG for different modes of response selection: Evidence from speech production and keyboard pressing

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