標題: Titlebook: EEG Signal Processing and Feature Extraction; Li Hu,Zhiguo Zhang Book 2019 Springer Nature Singapore Pte Ltd. 2019 Electroencephalography [打印本頁] 作者: lumbar-puncture 時間: 2025-3-21 16:19
書目名稱EEG Signal Processing and Feature Extraction影響因子(影響力)
書目名稱EEG Signal Processing and Feature Extraction影響因子(影響力)學科排名
書目名稱EEG Signal Processing and Feature Extraction網絡公開度
書目名稱EEG Signal Processing and Feature Extraction網絡公開度學科排名
書目名稱EEG Signal Processing and Feature Extraction被引頻次
書目名稱EEG Signal Processing and Feature Extraction被引頻次學科排名
書目名稱EEG Signal Processing and Feature Extraction年度引用
書目名稱EEG Signal Processing and Feature Extraction年度引用學科排名
書目名稱EEG Signal Processing and Feature Extraction讀者反饋
書目名稱EEG Signal Processing and Feature Extraction讀者反饋學科排名
作者: Pcos971 時間: 2025-3-22 00:10 作者: certain 時間: 2025-3-22 02:04 作者: fibroblast 時間: 2025-3-22 07:41 作者: Gum-Disease 時間: 2025-3-22 09:11 作者: blackout 時間: 2025-3-22 15:14
Indigenous Spirituality at Work: Australiaepochs that are time-locked to the specific events of interest should be extracted to facilitate the investigation of task/stimulus-related changes in EEG. Trials contaminated by artifacts, as well as bad channels that are not functioning properly for various reasons, should be excluded from further作者: blackout 時間: 2025-3-22 17:04 作者: Amenable 時間: 2025-3-22 21:36 作者: 猛擊 時間: 2025-3-23 04:08
Uses of Ultrasound and their Hazards, at different situations, it is still improper to determine which measure is the best one. The measure selection in your study should take a number of factors into account, such as the parameter selection, robust to artifacts, compute consumption, the correlation of the nonlinear characteristics wit作者: 衰老 時間: 2025-3-23 05:37 作者: 慷慨援助 時間: 2025-3-23 10:51 作者: convulsion 時間: 2025-3-23 15:08
Statistical Analysis,electing the right statistical procedures would be important in EEG signal analysis. Finally, we discussed the problem of multiple comparison, which may increase the probability for the researcher to get to false positive conclusion due to the multiple comparison and remains a challenge in statistical method.作者: Needlework 時間: 2025-3-23 19:59
Book 2019eloped. Intended for cognitive neuroscientists, psychologists and other interested readers, the book discusses a range of current mainstream EEG signal-processing and feature-extraction techniques in depth, and includes chapters on the principles and implementation strategies..作者: Spina-Bifida 時間: 2025-3-24 00:58
Book 2019prehensive, simple, and easy-to-understand manner. EEG records the electrical activity generated by the firing of neurons within human brain at the scalp. They are widely used in clinical neuroscience, psychology, and neural engineering, and a series of EEG signal-processing techniques have been dev作者: nullify 時間: 2025-3-24 05:48 作者: 權宜之計 時間: 2025-3-24 09:33
of EPs and ERPs is provided, and classical EP and ERP components that are applied in clinical and neuroscience studies are also described, with an emphasis on different sensory modalities through which the stimuli are presented. Finally, the pitfalls and promise in EP and ERP studies are discussed.作者: 我還要背著他 時間: 2025-3-24 11:59
Erika Takada,Judy M. Ford,Linda S. Lloydsition, to provide a comprehensive review on this growing topic. The main focus will be on basic principles of applying ICA on continuous EEG data to remove artifacts, PCA, and tensor decomposition on ERP data to conduct group analysis. The introduction of current softwares specialized in PCA and ICA on EEG signal processing will also be covered.作者: conduct 時間: 2025-3-24 17:22 作者: Gingivitis 時間: 2025-3-24 20:58 作者: 美麗的寫 時間: 2025-3-24 23:57 作者: 吝嗇性 時間: 2025-3-25 04:52
fier, evaluating results, and pattern expression. We also discuss perspective, particularly the deep learning algorithms, for future study. In the last section of this chapter, we give detailed MATLAB codes for implementing machine learning analysis for classifying eyes-open and eyes-closed EEG data.作者: Obscure 時間: 2025-3-25 10:31 作者: confederacy 時間: 2025-3-25 12:09 作者: DAFT 時間: 2025-3-25 17:49 作者: 配偶 時間: 2025-3-25 23:50
Machine Learning,fier, evaluating results, and pattern expression. We also discuss perspective, particularly the deep learning algorithms, for future study. In the last section of this chapter, we give detailed MATLAB codes for implementing machine learning analysis for classifying eyes-open and eyes-closed EEG data.作者: BRACE 時間: 2025-3-26 03:21
Electroencephalography, Evoked Potentials, and Event-Related Potentials,of EPs and ERPs is provided, and classical EP and ERP components that are applied in clinical and neuroscience studies are also described, with an emphasis on different sensory modalities through which the stimuli are presented. Finally, the pitfalls and promise in EP and ERP studies are discussed.作者: Incompetent 時間: 2025-3-26 07:55
Blind Source Separation,sition, to provide a comprehensive review on this growing topic. The main focus will be on basic principles of applying ICA on continuous EEG data to remove artifacts, PCA, and tensor decomposition on ERP data to conduct group analysis. The introduction of current softwares specialized in PCA and ICA on EEG signal processing will also be covered.作者: 得體 時間: 2025-3-26 10:19
Xiyue Peng,Lianzhou Wang,Bin Luomy personal experience. Finally, I introduce some well-known experimental tasks that have been proven to reliably elicit specific ERP components. Researchers may consider developing their studies based on the classic paradigms.作者: 誘導 時間: 2025-3-26 15:26
Denise Frizzell,David K. Bannertions. Three different types of brain networks were established, and further studies on the relationship between structural brain networks and functional brain networks, as well as brain network research based on computational models. Finally, we discussed the future research directions of spatial complex brain networks.作者: 羊齒 時間: 2025-3-26 20:10 作者: Small-Intestine 時間: 2025-3-26 22:55
Mental Health Assessment of Juveniles,tation can be used to identify the underlying ERP components and their latencies from the multichannel ERP waveforms. In this chapter, we illustrated the basic concepts of microstate analysis, the commonly used clustering algorithms, the metrics derived from microstate analysis, and how to perform microstate analysis using open-access tools.作者: ETHER 時間: 2025-3-27 04:47
Spirituality, Belief, and Relationship convolutional neural network and recurrent neural network. Then, we provide the applications of these two DL models to focus on the eye state detection task, which both achieve excellent recognition effects and are expected to be useful for broader applications in BCI systems.作者: Forage飼料 時間: 2025-3-27 07:35 作者: 去世 時間: 2025-3-27 09:36 作者: 飛鏢 時間: 2025-3-27 13:54 作者: Cantankerous 時間: 2025-3-27 21:07
Deep Learning, convolutional neural network and recurrent neural network. Then, we provide the applications of these two DL models to focus on the eye state detection task, which both achieve excellent recognition effects and are expected to be useful for broader applications in BCI systems.作者: Emasculate 時間: 2025-3-28 00:07 作者: 坦白 時間: 2025-3-28 02:24
EEG/ERP Data Analysis Toolboxes,lboxes in EEG/ERP analysis, such as EEGLab, FieldTrip, BrainVision Analyzer, etc., and then focus on the introduction of Letswave, which is an intuitive and streamlined tool to process and visualize EEG data, with a shallow learning curve. Examples are provided for a better understanding of Letswave7 in EEG/ERP data analysis.作者: Mosaic 時間: 2025-3-28 09:40
https://doi.org/10.1007/978-981-13-9113-2Electroencephalography (EEG); Signal processing; Feature extraction; Time series analysis; Software作者: 無辜 時間: 2025-3-28 10:45
978-981-13-9115-6Springer Nature Singapore Pte Ltd. 2019作者: anatomical 時間: 2025-3-28 15:17 作者: 進入 時間: 2025-3-28 18:50 作者: adj憂郁的 時間: 2025-3-28 23:09 作者: atopic-rhinitis 時間: 2025-3-29 06:25
A. Davies,K. F. Martin,P. Thorpe of EEG is summarized. Then, issues about volume conduction and source estimation of EEG are discussed. Finally, the fundamentals of EEG measurement and the methods for improving performance of EEG measurement are provided.作者: Mutter 時間: 2025-3-29 09:04 作者: LAP 時間: 2025-3-29 12:38 作者: esthetician 時間: 2025-3-29 15:51 作者: Stress-Fracture 時間: 2025-3-29 22:23
re analyzed in the frequency domain. The spectral analysis can transform EEG signals from time domain to the frequency domain, which can reveal how the power of EEG signals is distributed along frequencies. Furthermore, as EEG spectrum could substantially vary over time, joint time-frequency analysi作者: POLYP 時間: 2025-3-30 01:37 作者: 顯示 時間: 2025-3-30 07:00 作者: 領袖氣質 時間: 2025-3-30 11:56
mpling of electromagnetic brain signals in milliseconds has already been achieved. Unfortunately, the spatial resolution of EEG is very poor, which is limited by the relatively small number of spatial measurements (only a few hundred in EEG) and the inherent ambiguity of the underlying static electr作者: Daily-Value 時間: 2025-3-30 15:26
ce the traditionally used across-trial averaging approach could lead to the loss of the information concerning across-trial variability of both phase-locked ERP and non-phase-locked ERS/ERD responses. In this chapter, we provided the technical details of single-trial analysis methods both in the tim作者: Noctambulant 時間: 2025-3-30 18:31
Uses of Ultrasound and their Hazards,ntribute to the understanding of the EEG dynamics and the underlying brain processes. Until now, a number of nonlinear dynamic methods have been proposed. These methods reveal various nonlinear properties of the EEG signals. Among them, “complexity” and “entropy” are the widely used concept in the E作者: 不斷的變動 時間: 2025-3-30 22:35 作者: senile-dementia 時間: 2025-3-31 04:26 作者: pericardium 時間: 2025-3-31 07:20 作者: Morbid 時間: 2025-3-31 09:30
brain states and extract them from non-informative high-dimensional EEG data. Given the growth in the interest and breadth of application, we introduce how to apply machine learning techniques in EEG analysis. First, we give an overview of machine learning analysis and introduce several basic conce作者: 凝結劑 時間: 2025-3-31 16:40
Spirituality, Belief, and Relationshipwith traditional methods in classification tasks is receiving unsatisfactory recognition effects from EEG signals. In recent years, deep learning has drawn a great deal of attentions in diverse research fields, and could provide a novel solution for learning robust representations from EEG signals. 作者: 注意力集中 時間: 2025-3-31 17:40
the descriptive statistical methods for presenting the result from the raw data. Furthermore, analysis techniques comprised of parametric strategies like t-test, ANOVA, regression, and nonparametric procedures, such as permutation test, are introduced with their implementation in MATLAB and SPSS. S作者: indifferent 時間: 2025-3-31 22:10
