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Analyzing neural time series data : theory and practice

Cohen, Mike X.
Tipo de material: materialTypeLabelLibroSeries: . Inf. Publicación: Cambridge (Massachusetts) :London MIT , cop. ,c2014Descripción fisica: XVIII, 578 paginas :il graficas ;24 cm.ISBN: 9780262019873.Tema(s): Neural networks (Neurobiology) | Neural networks (Computer science) | Computational neuroscience
Contenidos:
c 1. The purpose of this book,who should read it, and how to use it.--c 2. Advantages and limitations of time -and time -frequency-domain analyses. --c 3. Interpreting and asking questions about -time -frequency results.--c 4. Introduction to matlab programming. --c 5. ntroduction to the physiological bases of EEG .--c 6. Practicallities of EEG measurement and experiment design. --c 7. Preprocessing steps necessary and useful for advanced data analysis.--c 8. EEG artfacts : their dectections, influence, and removal. --c 9. Overview of time domán EEG analyses .--c 10. The dot product and convolution . --c 11. The discrete time fourier transform, the FFT and the convolution theorem.--c 12. Molet wave. --c13. Complex Morlet Wavelets and Extracting Power and Phase. --c14. Bandpass Filtering and the Hilbert Transform . --c15. Short-Time FFT. --c 16. Multitapers --c17. Autoregressive Modeling . --c 18. Transform --c 19. Intertrial Phase Clustering -c 20. . Differences among Total, Phase-Locked, and Non-Phase-Locked Power and Intertrial Phase Consistency --c 21.. Interpretations and Limitations of Time-Frequency Power and ITPC Analyses -c 22. Surface Laplacian. --c 23. Principal Components Analysis --c 24. Basics of Single-Dipole and Distributed-Source Imaging --c 25. Introduction to the Various Connectivity Analyses --c 26. Terminology --c 27. Power-Based Connectivity --c 28. Granger Prediction --c 29. Mutual Information --c 30. Cross-Frequency Coupling -- 31. Graph Theory --c 32. Advantages and Limitations of Different Statistical Procedures --c 33. Within-Subject Statistical Analyses --c 34. Group-Level Analyses --c 35. Reporting Results in the Results Section --c 36. Recommendations for Reporting Results in Figures, Tables, and Text --c 37. Recurring Themes in This Book and Some Personal Advice --c 38. The Future of Cognitive Electrophysiology .
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c 1. The purpose of this book,who should read it, and how to use it.--c 2. Advantages and limitations of time -and time -frequency-domain analyses. --c 3. Interpreting and asking questions about -time -frequency results.--c 4. Introduction to matlab programming. --c 5. ntroduction to the physiological bases of EEG .--c 6. Practicallities of EEG measurement and experiment design. --c 7. Preprocessing steps necessary and useful for advanced data analysis.--c 8. EEG artfacts : their dectections, influence, and removal. --c 9. Overview of time domán EEG analyses .--c 10. The dot product and convolution . --c 11. The discrete time fourier transform, the FFT and the convolution theorem.--c 12. Molet wave. --c13. Complex Morlet Wavelets and Extracting Power and Phase. --c14. Bandpass Filtering and the Hilbert Transform . --c15. Short-Time FFT. --c 16. Multitapers --c17. Autoregressive Modeling . --c 18. Transform --c 19. Intertrial Phase Clustering -c 20. . Differences among Total, Phase-Locked, and Non-Phase-Locked Power and Intertrial Phase Consistency --c 21.. Interpretations and Limitations of Time-Frequency Power and ITPC Analyses -c 22. Surface Laplacian. --c 23. Principal Components Analysis --c 24. Basics of Single-Dipole and Distributed-Source Imaging --c 25. Introduction to the Various Connectivity Analyses --c 26. Terminology --c 27. Power-Based Connectivity --c 28. Granger Prediction --c 29. Mutual Information --c 30. Cross-Frequency Coupling -- 31. Graph Theory --c 32. Advantages and Limitations of Different Statistical Procedures --c 33. Within-Subject Statistical Analyses --c 34. Group-Level Analyses --c 35. Reporting Results in the Results Section --c 36. Recommendations for Reporting Results in Figures, Tables, and Text --c 37. Recurring Themes in This Book and Some Personal Advice --c 38. The Future of Cognitive Electrophysiology .

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