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Advanced signal processing on brain event-related potentials : filtering ERPs in time, frequency and space domains sequentially and simultaneously /
"This book is devoted to the application of advanced signal processing on event-related potentials (ERPs) in the context of electroencephalography (EEG) for the cognitive neuroscience. ERPs are usually produced through averaging single-trials of preprocessed EEG, and then, the interpretation of...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hackensack, NJ :
World Scientific,
[2015]
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 049 | |a UAMI | ||
| 100 | 1 | |a Cong, Fengyu, |e author. | |
| 245 | 1 | 0 | |a Advanced signal processing on brain event-related potentials : |b filtering ERPs in time, frequency and space domains sequentially and simultaneously / |c Fengyu Cong, Tapani Ristaniemi, Heikki Lyytinen. |
| 264 | 1 | |a Hackensack, NJ : |b World Scientific, |c [2015] | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Online resource; title from PDF title page (Ebsco, viewed April 15, 2015). | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Preface; List of Abbreviations; Chapter 1 Introduction; 1.1 Motivation; 1.1.1 Categories of EEG data; 1.1.2 Signal processing of EEG data; 1.2 Example of Conventional ERP Data Processing; 1.3 Linear Transform Model of ERP Data; 1.4 Existing Problems in Conventional ERP Data Processing and Their Solutions; 1.4.1 Assumptions for the averaging step; 1.4.2 Problems in the assumptions of the averaging step; 1.4.3 Solutions; 1.5 ERP Data for the Demonstration inThis Book; References. | |
| 505 | 8 | |a Chapter 2 Wavelet Filter Design Based on Frequency Responses for Filtering ERP DataWith Duration of One Epoch2.1 Correlation; 2.2 Impulse Response and Frequency Response; 2.3 Moving-Average Model-Based FIR Digital Filter; 2.3.1 Interpreting the digital filter in terms of correlation; 2.3.2 Problems of the digital filter in removing artifacts and their solutions; 2.4 DFT-Based Digital Filter; 2.4.1 Definition of DFT; 2.4.2 Interpreting DFT using correlation; 2.4.3 DFT-based digital filter; 2.4.4 Problems of the DFT filter and their corresponding solutions; 2.5 Wavelet Transform. | |
| 505 | 8 | |a 2.5.1 Definition of wavelet transform2.5.2 Interpreting the wavelet transform using correlation; 2.5.3 Differences between the Fourier and wavelet transforms; 2.5.4 Implementation of DWT; 2.6 Wavelet Filter Design Based on Frequency Response; 2.6.1 Introduction to wavelet filter; 2.6.2 Key issues in the wavelet filter design; 2.6.3 Determination of the number of levels; 2.6.3.1 Existing problem and current solution; 2.6.3.2 New solution; 2.6.4 Frequency division at different DWT levels: Overlapped frequency contents at different levels. | |
| 505 | 8 | |a 2.6.5 Frequency division in the first level of DWT: The cutoff frequency of the LP and HP filters is Fs/2 instead of Fs/42.6.6 Selection of the detail coefficients at some levels for signal reconstruction; 2.6.6.1 Existing problem and current solution; 2.6.6.2 New solution; 2.6.7 Choosing the wavelet for the wavelet filter in ERP studies; 2.6.7.1 Existing problem and current solution; 2.6.7.2 New solution; 2.6.8 Effect of sampling frequency on the wavelet filter; 2.7 Linear Superposition Rule of the Wavelet Filter and Benefit of the Wavelet Filter in Contrast to the Digital Filter. | |
| 505 | 8 | |a 2.8 Comparison Between the Wavelet and Digital Filters: Case Study on the Waveform and Magnitude Spectrum2.9 Recommendation for the Wavelet Filter Design; 2.10 Summary: ERP Data Processing Approach Using DFT or Wavelet Filter; 2.11 Existing Key Problem and Potential Solution; 2.12 MATLABCodes; 2.12.1 DFT filter function; 2.12.2 Wavelet filter function; 2.12.3 Frequency responses of DFT filter and wavelet filter; References; Chapter 3 Individual-Level ICA to Extract the ERP Components from the Averaged EEG Data; 3.1 Classic ICA Theory; 3.1.1 Brief history. | |
| 520 | |a "This book is devoted to the application of advanced signal processing on event-related potentials (ERPs) in the context of electroencephalography (EEG) for the cognitive neuroscience. ERPs are usually produced through averaging single-trials of preprocessed EEG, and then, the interpretation of underlying brain activities is based on the ordinarily averaged EEG. We find that randomly fluctuating activities and artifacts can still present in the averaged EEG data, and that constant brain activities over single trials can overlap with each other in time, frequency and spatial domains. Therefore, before interpretation, it will be beneficial to further separate the averaged EEG into individual brain activities. The book proposes systematic approaches pre-process wavelet transform (WT), independent component analysis (ICA), and nonnegative tensor factorization (NTF) to filter averaged EEG in time, frequency and space domains to sequentially and simultaneously obtain the pure ERP of interest. Software of the proposed approaches will be open-accessed."-- |c Provided by publisher. | ||
| 590 | |a Knovel |b ACADEMIC - Biochemistry, Biology & Biotechnology | ||
| 650 | 0 | |a Evoked potentials (Electrophysiology) | |
| 650 | 0 | |a Electroencephalography. | |
| 650 | 0 | |a Signal processing. | |
| 650 | 2 | |a Evoked Potentials | |
| 650 | 2 | |a Electroencephalography | |
| 650 | 6 | |a Potentiels évoqués (Électrophysiologie) | |
| 650 | 6 | |a Électroencéphalographie. | |
| 650 | 6 | |a Traitement du signal. | |
| 650 | 7 | |a HEALTH & FITNESS |x Diseases |x Nervous System (incl. Brain) |2 bisacsh | |
| 650 | 7 | |a MEDICAL |x Neurology. |2 bisacsh | |
| 650 | 7 | |a Electroencephalography |2 fast | |
| 650 | 7 | |a Evoked potentials (Electrophysiology) |2 fast | |
| 650 | 7 | |a Signal processing |2 fast | |
| 700 | 1 | |a Ristaniemi, Tapani, |e author. | |
| 700 | 1 | |a Lyytinen, Heikki, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Cong, Fengyu. |t Advanced signal processing on brain event-related potentials : filtering ERPs in time, frequency and space domains sequentially and simultaneously. |d Singapore : World Scientific Publishing Co. Pte. Ltd., ©2015 |h xxi, 202 pages |z 9789814623087 |
| 856 | 4 | 0 | |u https://appknovel.uam.elogim.com/kn/resources/kpASPBERPA/toc |z Texto completo |
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