Brain Institute, Federal University of Rio Grande do Norte

This repository is part of an introductory course on signal analysis for undegraduate and graduate students

1. Lesson 01
   • Course outline
   • Electrophysiology basics
   • Signal analysis applications in neuroscience
   • Introduction to Matlab
2. Lesson 02
   • Complex plane representations
   • Fourier transform
   • Power spectrum
3. Lesson 03
   • Time-frequency resolution (uncertainty principle)
   • Discrete time-frequency decomposition
   • Welch's periodogram
   • List of exercises 01
4. Lesson 04
   • Time-frequency decomposition
   • Normalizations
   • Baseline normalization
   • List of exercises 02
5. Lesson 05
   • Phase coherence
   • Coherence spectrum
   • Coherograms
6. Lesson 06
   • Signal sampling
   • Aliasing
   • Nyquist frequency
   • List of exercises 03 (first course assessment)
7. Lesson 07
   • Convolutions
   • Edge effects
   • Introduction to filters
8. Lesson 08
   • Filters
   • Low-pass and high-pass filters
   • Band-pass filters
   • Notch filters
9. Lesson 09
   • Hilbert transform
   • Amplitude envelopes
   • Amplitude spectrum
   • List of exercises 04
10. Lesson 10
    • Introduction to wavelets
    • Morlet wavelets
    • Continuous TFD via wavelets
11. Lesson 11
    • Intantaneous phase and frequency
    • Phase-locking metrics
    • Phase-locking value (PLV)
    • List of exercises 05 (second course assessment)
12. Lesson 12
    • PLV spectrum
    • Von Mises distributions
    • Modulation Index (MI)
    • Inter-trial coherence (ITC)
13. Lesson 13
    • Auto-correlogram (ACG)
    • Cross-correlogram (CCG)
    • CCG normalization
    • Relationship between CCG and Power
14. Lesson 14
    • n:m Phase-Phase coupling
    • Phase-Amplitude coupling (PAC)
    • Amplitude-Amplitude coupling
    • Comodulograms