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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 |
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| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hackensack, NJ :
World Scientific,
[2015]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.