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30-Day Money-Back Guarantee
Teaching & Academics Science Data Analysis

Complete neural signal processing and analysis: Zero to hero

Learn signal processing and statistics using brain electrical data with expert instruction and code challenges in MATLAB
Bestseller
Rating: 4.8 out of 54.8 (594 ratings)
2,874 students
Created by Mike X Cohen
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

What you'll learn

  • Signal processing
  • Time series data analysis
  • Statistics (non-parametric)
  • Neuroscience (brain science)
  • Spectral analysis application
  • Applied math
Curated for the Udemy for Business collection

Requirements

  • Basic MATLAB knowledge
  • Access to MATLAB or Octave

Description

Use your brain to learn signal processing, data analysis, and statistics... by learning about brains!

If you are reading this, I guess you have a brain. Your brain generates electrical signals that can be measured using electrodes, which are like small antennas. These electrical signals are rreeeeeaaallly complicated, because the brain is really complicated! 

But learning how to analyze brain electrical signals is an amazing and fascinating way to learn about signal processing, data visualization, spectral analysis, synchronization (connectivity) analyses, and statistics (in particular, permutation-based statistics).


What do you get in this course?

  • This course contains over 46 hours of video instruction, plus TONS of MATLAB exercises, problem sets, and challenges.

  • If you do all the MATLAB exercises, this course is easily well over 100 hours of educational content.

  • And you get access to the Q&A forum, where you can post specific questions about the course material and I answer as quickly as I can (typically 1-2 days).

  • By the end of this course, you will have confidence in processing, cleaning, analyzing, and performing statistics on brain electrical activity.


What do you need to know before joining this course?

I have tried to make this course accessible to anyone who is interested in learning neural signal processing and time series analysis.

I believe you can simply start this course without any formal background in neuroscience/biology, and without any background in signal processing/math/statistics. That said, some background in these topics will definitely be helpful.

However, I do assume that you have access to MATLAB (or Octave), and that you have some basic MATLAB coding skills (variables, for-loops, basic plotting). If you are a total noob to MATLAB, then please first take an intro-MATLAB course and then come back here.


Why should you trust this weird Mike X Cohen guy?

I've been teaching this material for almost 20 years. I'm really dedicated to teaching and I work really hard to improve my courses each year.

Check out the reviews of this course and my other courses to see what my students think of my teaching style and dedication.

I've also written several textbooks on neural data analysis and scientific programming. And there are more books and more courses on the way!

... but you have to watch out for my weird sense of humor. You've been warned...

Who this course is for:

  • Anyone interested in applied signal processing
  • Interested in non-parametric statistics
  • Existing or aspiring neuroscience students
  • Anyone who wants to know what brain electrical signals look like

Featured review

Mittu S
Mittu S
116 courses
5 reviews
Rating: 5.0 out of 59 months ago
I find Dr.Mike to be extremely articulate in his teaching and the courses have been interesting, informative and engaging. Having been in the signal processing field for a while, the courses have added positively to my learning experience and have helped widen my areas of study. Also, a special mention to the quick response time to discussion questions in the forum. I highly recommend the courses and look forward to more courses. Thank you, Dr. Mike! -Smita

Course content

14 sections • 233 lectures • 46h 54m total length

  • Preview08:00
  • Neural data science as source sepatation
    15:57
  • What to expect from this course
    11:58
  • A quick note about how this went from 2 to 1 course
    02:30
  • Download this file if you are using Octave (otherwise ignore)
    00:10

  • Download MATLAB materials for this course
    00:02
  • Origin, significance, and interpretation of EEG
    21:36
  • Overview of possible preprocessing steps
    13:39
  • ICA for data cleaning
    19:01
  • Signal artifacts (not) to worry about
    15:48
  • Preview12:27
  • Overview of time-domain analyses (ERPs)
    16:19
  • Motivations for rhythm-based analyses
    16:39
  • Interpreting time-frequency plots
    16:11
  • The empirical datasets used in this course
    04:26
  • MATLAB: EEG dataset
    19:31
  • MATLAB: V1 dataset
    08:14
  • Where to get more EEG data?
    04:10
  • Simulating data to understand analysis methods
    21:57
  • Problem set: introduction and explanation
    04:08
  • Problem set (1/2): Simulating and visualizing data
    35:18
  • Problem set (2/2): Simulating and visualizing data
    36:18
  • Planck, neuron, universe
    03:37

  • MATLAB files for this section
    00:02
  • Why simulate data?
    08:26
  • Generating white and pink noise
    12:24
  • The three important equations (sine, Gaussian, Euler's)
    23:01
  • Generating "chirps" (frequency-modulated signals)
    06:41
  • Non-stationary narrowband activity via filtered noise
    05:27
  • Transient oscillation
    07:22
  • The eeglab EEG structure
    12:58
  • Project 1-1: Channel-level EEG data
    09:30
  • Project 1-1: Solutions
    11:47
  • Projecting dipoles onto EEG electrodes
    09:05
  • Project 1-2: dipole-level EEG data
    04:50
  • Project 1-2: Solutions
    10:46

  • MATLAB files for this section
    00:02
  • Event-related potential (ERP)
    17:42
  • Lowpass filter an ERP
    16:54
  • Compute the average reference
    07:09
  • Butterfly plot and topo-variance time series
    06:11
  • Topography time series
    12:57
  • Preview09:37
  • Project 2-1: Quantify the ERP as peak-mean or peak-to-peak
    07:07
  • Project 2-1: Solutions
    15:19
  • Project 2-2: ERP peak latency topoplot
    02:30
  • Project 2-2: Solutions
    08:08

  • Download MATLAB materials for this section
    00:03
  • Course tangent: self-accountability in online learning
    03:03
  • Time and frequency domains
    09:42
  • Sine waves
    08:05
  • MATLAB: Sine waves and their parameters
    09:25
  • Complex numbers
    14:23
  • Euler's formula
    12:03
  • MATLAB: Complex numbers and Euler's formula
    12:24
  • The dot product
    09:40
  • MATLAB: Dot product and sine waves
    10:37
  • Complex sine waves
    04:51
  • Preview06:32
  • The complex dot product
    07:12
  • MATLAB: The complex dot product
    13:54
  • Fourier coefficients
    12:30
  • MATLAB: The discrete-time Fourier transform
    15:22
  • MATLAB: Fourier coefficients as complex numbers
    16:52
  • Frequencies in the Fourier transform
    12:22
  • Positive and negative frequencies
    14:33
  • Accurate scaling of Fourier coefficients
    08:33
  • MATLAB: Positive/negative spectrum; amplitude scaling
    17:27
  • MATLAB: Spectral analysis of resting-state EEG
    14:50
  • MATLAB: Quantify alpha power over the scalp
    18:51
  • The perfection of the Fourier transform
    09:45
  • The inverse Fourier transform
    06:51
  • MATLAB: Reconstruct a signal via inverse FFT
    10:05
  • Frequency resolution and zero-padding
    10:14
  • MATLAB: Frequency resolution and zero-padding
    16:38
  • Estimation errors and Fourier coefficients
    08:05
  • Signal nonstationarities
    13:06
  • MATLAB: Examples of sharp nonstationarities on power spectra
    10:31
  • MATLAB: Examples of smooth nonstationarities on power spectra
    17:10
  • Welch's method for smooth spectral decomposition
    11:13
  • MATLAB: Welch's method on phase-slip data
    11:47
  • Preview06:27
  • MATLAB: Welch's method on V1 dataset
    05:36
  • Problem set (1/2): Spectral analyses of real and simulated data
    33:00
  • Problem set (2/2): Spectral analyses of real and simulated data
    37:09

  • MATLAB files for this section
    00:02
  • Program the Fourier transform from scratch!
    04:18
  • Program the inverse Fourier transform from scratch!
    05:51
  • Spectral separation on simulated dipole data
    06:00
  • FFT of stationary and non-stationary simulated data
    12:37
  • FFT and Welch's method on EEG resting state data
    12:00
  • To taper or not to taper?
    16:50
  • Extracting average power from a frequency band
    04:53
  • Comparing average spectra vs. spectra of an average
    11:39
  • Project 3-1: Topography of spectrally separated activity
    04:22
  • Project 3-1: Solutions
    11:55
  • Project 3-2: Topography of alpha-theta ratio
    03:51
  • Project 3-2: Solutions
    11:21

  • Download MATLAB materials for this section
    00:03
  • Morlet wavelets in time and in frequency
    17:47
  • Preview14:09
  • Convolution in the time domain
    23:36
  • MATLAB: Time-domain convolution
    14:20
  • Convolution as spectral multiplication
    19:29
  • MATLAB: The five steps of convolution
    08:28
  • MATLAB: Convolve real data with a Gaussian
    12:56
  • MATLAB: Complex Morlet wavelets
    08:32
  • Complex Morlet wavelet convolution
    12:43
  • Convolution coding tips
    07:54
  • MATLAB: Complex Morlet wavelet convolution
    19:02
  • MATLAB: Convolution with all trials!
    08:26
  • MATLAB: A full time-frequency power plot!
    09:50
  • Averaging phase values
    13:06
  • Inter-trial phase clustering (ITPC/ITC)
    15:16
  • MATLAB: ITPC
    13:18
  • Parameters of Morlet wavelet (time-frequency trade-off)
    18:18
  • MATLAB: Time-frequency trade-off
    18:55
  • The stationarity assumption of wavelet convolution
    05:29
  • The "1/f" structure of spectral brain dynamics
    14:56
  • Baseline normalization of time-frequency power
    18:34
  • MATLAB: Baseline normalization of TF plots
    13:42
  • Scale-free dynamics via detrended fluctuation analysis (DFA)
    11:28
  • MATLAB: detrended fluctuation analysis
    21:18
  • The filter-Hilbert time-frequency method
    23:06
  • MATLAB: Filter-Hilbert
    17:27
  • The short-time Fourier transform (STFFT)
    07:33
  • MATLAB: STFFT
    07:40
  • Comparing wavelet, filter-Hilbert, and STFFT
    13:22
  • The multi-taper method
    11:03
  • Within-subject, cross-trial regression
    16:16
  • MATLAB: Cross-trial regression
    23:41
  • Temporal resolution vs. precision, pre- and post-convolution
    09:24
  • MATLAB: Downsampling time-frequency results
    17:06
  • MATLAB: Linear vs. logarithmic frequency scaling
    13:45
  • Separating phase-locked and non-phase-locked activity
    12:23
  • MATLAB: Total, non-phase-locked, and phase-locked power
    19:27
  • Edge effects, buffer zones, and data epoch length
    09:41
  • Problem set (1/3): Time-frequency analysis
    29:18
  • Problem set (2/3): Time-frequency analysis
    21:18
  • Problem set (3/3): Time-frequency analysis
    34:59

  • MATLAB files for this section
    00:02
  • Create a family of complex Morlet wavelets
    09:57
  • Create a time-frequency plot of a nonlinear chirp
    12:39
  • Preview09:57
  • Wavelet convolution of close frequencies
    11:24
  • Time-frequency power of multitrial EEG activity
    07:51
  • Baseline normalize power with dB and % change
    11:05
  • Exploring wavelet parameters in real data
    11:01
  • Exploring wavelet parameters in simulated data
    10:38
  • Inter-trial phase clustering before vs. after removing ERP
    09:44
  • Downsampling time-frequency power
    07:37
  • Visualize time-frequency power from all channels
    06:55
  • Instantaneous frequency in simulated data
    12:41
  • Instantaneous frequency in real data
    08:18
  • Project 4-1: Phase-locked, non-phase-locked, and total power
    04:36
  • Project 4-1: Solutions
    13:52
  • Narrowband filtering and the Hilbert transform
    12:20
  • Project 4-2: Time-frequency power plot via filter-Hilbert
    03:01
  • Project 4-2: Solutions
    07:37

  • Download MATLAB materials for this section
    00:01
  • Four things to keep in mind about connectivity
    16:11
  • Volume conduction and what to do about it
    10:29
  • Preview13:12
  • Inter-site phase clustering (ISPC)
    09:41
  • MATLAB: ISPC
    17:31
  • Surface Laplacian for connectivity analyses
    11:02
  • MATLAB: Laplacian in simulated data
    10:19
  • MATLAB: Laplacian in real EEG data
    14:44
  • Phase-lag-based connectivity
    12:13
  • MATLAB: phase-lag index
    14:39
  • When to use phase-lag vs. phase-clustering measures
    17:48
  • MATLAB: Phase synchronization in voltage and Laplacian data
    18:58
  • Connectivity over time vs. over trials
    07:16
  • MATLAB: Connectivity over time vs. over trials
    07:42
  • MATLAB: Simulating data to test connectivity methods
    17:37
  • Two methods of power-based connectivity
    10:19
  • Granger causality (prediction)
    32:17
  • MATLAB: Granger causality
    22:36
  • "Hubness" from graph theory
    12:57
  • MATLAB: Connectivity hubs
    22:59
  • When to use which connectivity method?
    06:19
  • Problem set (1/2): Pairwise synchronization
    22:29
  • Problem set (2/2): Pairwise synchronization
    32:07

  • MATLAB files for this section
    00:02
  • Synchronization in simulated noisy oscillators
    12:17
  • Preview12:04
  • Phase synchronization matrices in multitrial data
    15:40
  • Power time series correlations
    17:10
  • Power correlations over trials
    11:13
  • Scalp Laplacian for electrode-level connectivity
    10:42
  • All-to-all synchronization and "hubness" (graph theory)
    14:03
  • Phase-lag index
    14:45
  • Project 5-1: ISPC and PLI, with and without Laplacian
    05:08
  • Project 5-1: Solutions
    05:31
  • Project 5-2: Seeded phase vs. power coupling
    03:50
  • Project 5-2: Solutions
    07:36

Instructor

Mike X Cohen
Neuroscientist, writer, professor
Mike X Cohen
  • 4.6 Instructor Rating
  • 21,166 Reviews
  • 106,276 Students
  • 20 Courses

I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European Union, hospitals, and private organizations.

But you're here because of my teaching, so let me tell you about that: 

I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors. I teach in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >80 hours of online lectures on neuroscience data analysis that you can find on my website and youtube channel. And I've written several technical books about these topics with a few more on the way.

I'm not trying to show off -- I'm trying to convince you that you've come to the right place to maximize your learning from an instructor who has spent two decades refining and perfecting his teaching style.

Over 94,000 students have watched over 6,500,000 minutes of my courses (that's over 12 years of continuous learning). Come find out why!

I have several free courses that you can enroll in. Try them out! You got nothing to lose ;)

                                                  -------------------------

By popular request, here are suggested course progressions for various educational goals:

MATLAB programming: MATLAB onramp; Master MATLAB; Image Processing

Python programming: Master Python programming by solving scientific projects; Master Math by Coding in Python

Applied linear algebra: Complete Linear Algebra; Dimension Reduction

Signal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing

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