Complete neural signal processing and analysis: Zero to hero

Course Curriculum

Chapter 1: Introduction

Lecture 1: Broad introduction to neural time series analysis

Lecture 2: Neural data science as source sepatation

Lecture 3: What to expect from this course

Lecture 4: A quick note about how this went from 2 to 1 course

Lecture 5: Download this file if you are using Octave (otherwise ignore)

Chapter 2: The basics of neural signal processing

Lecture 1: Download MATLAB materials for this course

Lecture 2: Origin, significance, and interpretation of EEG

Lecture 3: Overview of possible preprocessing steps

Lecture 4: ICA for data cleaning

Lecture 5: Signal artifacts (not) to worry about

Lecture 6: Topographical mapping

Lecture 7: Overview of time-domain analyses (ERPs)

Lecture 8: Motivations for rhythm-based analyses

Lecture 9: Interpreting time-frequency plots

Lecture 10: The empirical datasets used in this course

Lecture 11: MATLAB: EEG dataset

Lecture 12: MATLAB: V1 dataset

Lecture 13: Where to get more EEG data?

Lecture 14: Simulating data to understand analysis methods

Lecture 15: Problem set: introduction and explanation

Lecture 16: Problem set (1/2): Simulating and visualizing data

Lecture 17: Problem set (2/2): Simulating and visualizing data

Lecture 18: Planck, neuron, universe

Chapter 3: Simulating time series signals and noise

Lecture 1: MATLAB files for this section

Lecture 2: Why simulate data?

Lecture 3: Generating white and pink noise

Lecture 4: The three important equations (sine, Gaussian, Euler's)

Lecture 5: Generating "chirps" (frequency-modulated signals)

Lecture 6: Non-stationary narrowband activity via filtered noise

Lecture 7: Transient oscillation

Lecture 8: The eeglab EEG structure

Lecture 9: Project 1-1: Channel-level EEG data

Lecture 10: Project 1-1: Solutions

Lecture 11: Projecting dipoles onto EEG electrodes

Lecture 12: Project 1-2: dipole-level EEG data

Lecture 13: Project 1-2: Solutions

Chapter 4: Time-domain analyses

Lecture 1: MATLAB files for this section

Lecture 2: Event-related potential (ERP)

Lecture 3: Lowpass filter an ERP

Lecture 4: Compute the average reference

Lecture 5: Butterfly plot and topo-variance time series

Lecture 6: Topography time series

Lecture 7: Simulate ERPs from two dipoles

Lecture 8: Project 2-1: Quantify the ERP as peak-mean or peak-to-peak

Lecture 9: Project 2-1: Solutions

Lecture 10: Project 2-2: ERP peak latency topoplot

Lecture 11: Project 2-2: Solutions

Chapter 5: Static spectral analysis

Lecture 1: Download MATLAB materials for this section

Lecture 2: Course tangent: self-accountability in online learning

Lecture 3: Time and frequency domains

Lecture 4: Sine waves

Lecture 5: MATLAB: Sine waves and their parameters

Lecture 6: Complex numbers

Lecture 7: Euler's formula

Lecture 8: MATLAB: Complex numbers and Euler's formula

Lecture 9: The dot product

Lecture 10: MATLAB: Dot product and sine waves

Lecture 11: Complex sine waves

Lecture 12: MATLAB: Complex sine waves

Lecture 13: The complex dot product

Lecture 14: MATLAB: The complex dot product

Lecture 15: Fourier coefficients

Lecture 16: MATLAB: The discrete-time Fourier transform

Lecture 17: MATLAB: Fourier coefficients as complex numbers

Lecture 18: Frequencies in the Fourier transform

Lecture 19: Positive and negative frequencies

Lecture 20: Accurate scaling of Fourier coefficients

Lecture 21: MATLAB: Positive/negative spectrum; amplitude scaling

Lecture 22: MATLAB: Spectral analysis of resting-state EEG

Lecture 23: MATLAB: Quantify alpha power over the scalp

Lecture 24: The perfection of the Fourier transform

Lecture 25: The inverse Fourier transform

Lecture 26: MATLAB: Reconstruct a signal via inverse FFT

Lecture 27: Frequency resolution and zero-padding

Lecture 28: MATLAB: Frequency resolution and zero-padding

Lecture 29: Estimation errors and Fourier coefficients

Lecture 30: Signal nonstationarities

Lecture 31: MATLAB: Examples of sharp nonstationarities on power spectra

Lecture 32: MATLAB: Examples of smooth nonstationarities on power spectra

Lecture 33: Welch's method for smooth spectral decomposition

Lecture 34: MATLAB: Welch's method on phase-slip data

Lecture 35: MATLAB: Welch's method on resting-state EEG data

Lecture 36: MATLAB: Welch's method on V1 dataset

Lecture 37: Problem set (1/2): Spectral analyses of real and simulated data

Lecture 38: Problem set (2/2): Spectral analyses of real and simulated data

Chapter 6: More on static spectral analyses

Lecture 1: MATLAB files for this section

Lecture 2: Program the Fourier transform from scratch!

Lecture 3: Program the inverse Fourier transform from scratch!

Lecture 4: Spectral separation on simulated dipole data

Lecture 5: FFT of stationary and non-stationary simulated data

Lecture 6: FFT and Welch's method on EEG resting state data

Lecture 7: To taper or not to taper?

Lecture 8: Extracting average power from a frequency band

Lecture 9: Comparing average spectra vs. spectra of an average