Titlebook: EEG Signal Processing and Feature Extraction; Li Hu,Zhiguo Zhang Book 2019 Springer Nature Singapore Pte Ltd. 2019 Electroencephalography

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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

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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

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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

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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

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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.

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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

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