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
- Description
- Curriculum
- FAQ
- Reviews
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…
The basics of neural signal processing
Simulating time series signals and noise
-
6
Download MATLAB materials for this course -
7
Origin, significance, and interpretation of EEG -
8
Overview of possible preprocessing steps
-
9
ICA for data cleaning
-
10
Signal artifacts (not) to worry about
-
11
Topographical mapping
-
12
Overview of time-domain analyses (ERPs)
-
13
Motivations for rhythm-based analyses
-
14
Interpreting time-frequency plots
-
15
The empirical datasets used in this course
-
16
MATLAB: EEG dataset
-
17
MATLAB: V1 dataset
-
18
Where to get more EEG data?
-
19
Simulating data to understand analysis methods
-
20
Problem set: introduction and explanation
-
21
Problem set (1/2): Simulating and visualizing data
-
22
Problem set (2/2): Simulating and visualizing data
-
23
Planck, neuron, universe
Time-domain analyses
-
24
MATLAB files for this section
-
25
Why simulate data?
-
26
Generating white and pink noise
-
27
The three important equations (sine, Gaussian, Euler's)
-
28
Generating "chirps" (frequency-modulated signals)
-
29
Non-stationary narrowband activity via filtered noise
-
30
Transient oscillation
-
31
The eeglab EEG structure
-
32
Project 1-1: Channel-level EEG data
-
33
Project 1-1: Solutions
-
34
Projecting dipoles onto EEG electrodes
-
35
Project 1-2: dipole-level EEG data
-
36
Project 1-2: Solutions
Static spectral analysis
-
37
MATLAB files for this section
-
38
Event-related potential (ERP)
-
39
Lowpass filter an ERP
-
40
Compute the average reference
-
41
Butterfly plot and topo-variance time series
-
42
Topography time series
-
43
Simulate ERPs from two dipoles
-
44
Project 2-1: Quantify the ERP as peak-mean or peak-to-peak
-
45
Project 2-1: Solutions
-
46
Project 2-2: ERP peak latency topoplot
-
47
Project 2-2: Solutions
More on static spectral analyses
-
48
Download MATLAB materials for this section
-
49
Course tangent: self-accountability in online learning
-
50
Time and frequency domains
-
51
Sine waves
-
52
MATLAB: Sine waves and their parameters
-
53
Complex numbers
-
54
Euler's formula
-
55
MATLAB: Complex numbers and Euler's formula
-
56
The dot product
-
57
MATLAB: Dot product and sine waves
-
58
Complex sine waves
-
59
MATLAB: Complex sine waves
-
60
The complex dot product
-
61
MATLAB: The complex dot product
-
62
Fourier coefficients
-
63
MATLAB: The discrete-time Fourier transform
-
64
MATLAB: Fourier coefficients as complex numbers
-
65
Frequencies in the Fourier transform
-
66
Positive and negative frequencies
-
67
Accurate scaling of Fourier coefficients
-
68
MATLAB: Positive/negative spectrum; amplitude scaling
-
69
MATLAB: Spectral analysis of resting-state EEG
-
70
MATLAB: Quantify alpha power over the scalp
-
71
The perfection of the Fourier transform
-
72
The inverse Fourier transform
-
73
MATLAB: Reconstruct a signal via inverse FFT
-
74
Frequency resolution and zero-padding
-
75
MATLAB: Frequency resolution and zero-padding
-
76
Estimation errors and Fourier coefficients
-
77
Signal nonstationarities
-
78
MATLAB: Examples of sharp nonstationarities on power spectra
-
79
MATLAB: Examples of smooth nonstationarities on power spectra
-
80
Welch's method for smooth spectral decomposition
-
81
MATLAB: Welch's method on phase-slip data
-
82
MATLAB: Welch's method on resting-state EEG data
-
83
MATLAB: Welch's method on V1 dataset
-
84
Problem set (1/2): Spectral analyses of real and simulated data
-
85
Problem set (2/2): Spectral analyses of real and simulated data
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
Stars 5
360
Stars 4
110
Stars 3
27
Stars 2
2
Stars 1
1
Course details
Video
47 hours
Lectures
14
Certificate of Completion
Popular courses
Social Network