Complete neural signal processing and analysis: Zero to hero from Udemy

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...

What's inside

Learning objectives

Signal processing
Time series data analysis
Statistics (non-parametric)

Neuroscience (brain science)
Spectral analysis application
Applied math

Signal processing
Time series data analysis
Statistics (non-parametric)
Neuroscience (brain science)
Spectral analysis application
Applied math

Syllabus

Introduction

Broad introduction to neural time series analysis

Neural data science as source sepatation

What to expect from this course

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides hands-on experience with MATLAB exercises, problem sets, and challenges, which allows learners to apply theoretical knowledge to practical scenarios

Assumes basic MATLAB coding skills, so learners without this background may need to acquire it before fully benefiting from the course

Covers spectral analysis, which is a fundamental technique for understanding the frequency components of neural signals and their implications

Explores time-domain analyses like Event-Related Potentials (ERPs), which are essential for understanding neural responses to specific events or stimuli

Includes coverage of Independent Component Analysis (ICA) for data cleaning, which is a crucial step in removing artifacts from neural recordings

Uses EEG data, which requires learners to understand the practical aspects of acquiring, preprocessing, and interpreting this type of neural data

Reviews summary

In-depth neural signal processing in matlab

According to students, this course provides amazing depth and is a comprehensive guide to neural signal processing and analysis using MATLAB. Learners particularly highlight the instructor, Mike X Cohen, praising his fantastic teaching style, clear explanations, and active presence in the Q&A forum where he provides prompt support. The numerous MATLAB exercises and hands-on projects are frequently mentioned as essential for understanding and applying the concepts, though some students found them challenging and noted the course requires significant time investment. While titled "Zero to hero", a recurring point is that while it builds concepts from the ground up, having some prior background in basic MATLAB, math, or statistics is highly beneficial, and lacking this may present a warning for absolute beginners. Overall, the sentiment is largely positive for those seeking a thorough and practical learning experience in this specialized field.

Demands substantial effort and time.

"Requires significant time investment outside the lectures for the problem sets."

"Be prepared to dedicate substantial time to the coding challenges."

"This isn't a quick course; it requires serious dedication."

Difficult material, worth the effort.

"Challenging course, but incredibly rewarding for those who stick with it."

"Definitely not easy, but the payoff in understanding is huge."

"Struggled at times, but the final understanding is solid."

Practical application through coding.

"The MATLAB exercises are challenging but essential."

"The hands-on projects are invaluable. Best course on neural signal processing I've found."

"Learned a lot about applying signal processing techniques to real brain data."

Clear teaching and responsive help.

"Mike's teaching style is fantastic - engaging and knowledgeable."

"Great instructor support on the forum."

"The forum is active and Mike answers questions."

Covers topics thoroughly from basics.

"Amazing depth! Mike explains complex topics clearly."

"Very comprehensive course. Covers a lot of ground from basics to advanced topics."

"This course provides a great foundation and goes deep into topics."

Basic MATLAB/math/stats helpful.

"Assumes too much prior knowledge in math and stats despite the title."

"Assumes some MATLAB proficiency, which was a bit tough for me initially."

"While 'zero to hero' conceptually, prerequisite knowledge helps handle pace."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete neural signal processing and analysis: Zero to hero with these activities:

Review Basic MATLAB Coding

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Strengthen your MATLAB foundation to better handle the coding exercises in the course.

Browse courses on MATLAB

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Review variables, for-loops, and basic plotting in MATLAB.
Practice writing simple scripts to generate and visualize data.
Familiarize yourself with MATLAB's documentation and help resources.

Review Basic MATLAB Coding

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Reinforce fundamental MATLAB skills like variables, loops, and plotting to ensure a smooth learning experience in the course.

Browse courses on MATLAB

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Complete online MATLAB tutorials covering basic syntax and data structures.
Practice writing simple scripts to perform calculations and create visualizations.
Review examples of for-loops and conditional statements in MATLAB.

Read "Statistics" by David Freedman, Robert Pisani, and Roger Purves

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Build a strong statistical foundation to better understand the statistical methods used in neural signal processing.

View Statistics, 4th Edition on Amazon

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Read the chapters on descriptive statistics and hypothesis testing.
Work through the examples and exercises in the book.
Relate the statistical concepts to the types of data encountered in neural signal processing.

Eight other activities

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Brush Up on Linear Algebra Fundamentals

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Strengthen your understanding of linear algebra concepts, which are essential for signal processing and data analysis techniques used in the course.

Browse courses on Linear Algebra

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Review matrix operations such as addition, multiplication, and transposition.
Study vector spaces, linear independence, and eigenvalues/eigenvectors.
Work through practice problems involving matrix decomposition and solving linear systems.

Compile a Glossary of Neural Signal Processing Terms

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Create a personal glossary to reinforce understanding of key concepts and terminology introduced in the course.

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As you progress through the course, note down unfamiliar terms and concepts.
Research and define each term in your own words.
Organize the glossary alphabetically or by topic.
Review and update the glossary regularly.

Read 'Statistics for Data Science' by James D. Miller

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Solidify your understanding of statistical concepts used in the course, particularly non-parametric statistics.

View Mastering Splunk 8 on Amazon

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Read the chapters on hypothesis testing and statistical significance.
Work through the examples and exercises provided in the book.
Relate the statistical concepts to the analysis of neural signals.

Practice Spectral Analysis on Simulated Data

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Reinforce your understanding of spectral analysis by applying it to simulated neural data.

Browse courses on Spectral Analysis

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Generate simulated neural data with known frequency components.
Apply Fourier transform and Welch's method to the simulated data.
Compare the results with the known frequency components.
Experiment with different parameters and settings.

Participate in a Study Group

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Reinforce learning through collaborative problem-solving and discussion of course concepts with peers.

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Form a study group with other students in the course.
Schedule regular meetings to discuss course materials and work through exercises.
Share insights and help each other understand challenging topics.

Create a Blog Post on ERP Analysis

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Deepen your understanding of event-related potentials (ERPs) by explaining the concepts and analysis techniques in a clear and concise manner.

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Research and gather information on ERP components and their significance.
Write a blog post explaining the basics of ERP analysis, including preprocessing steps and interpretation of results.
Include visualizations and examples to illustrate key concepts.

Implement a Spectral Analysis Pipeline

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Solidify your knowledge of spectral analysis techniques by implementing a complete pipeline for analyzing neural signals.

Browse courses on Spectral Analysis

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Choose a dataset of neural signals (e.g., EEG data).
Implement preprocessing steps such as filtering and artifact removal.
Apply spectral analysis techniques (e.g., FFT, Welch's method) to the data.
Visualize and interpret the results.

Read 'Analyzing Neural Time Series Data' by Mike X Cohen

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Deepen your understanding of neural time series analysis with a comprehensive guide written by the course instructor.

View MATLAB for Brain and Cognitive Scientists (Mit... on Amazon

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Read the chapters relevant to the topics covered in the course.
Work through the examples and exercises provided in the book.
Implement the analysis techniques using MATLAB.

Career center

Learners who complete Complete neural signal processing and analysis: Zero to hero will develop knowledge and skills that may be useful to these careers:

Psychophysiologist

Psychophysiologists study the relationship between physiological processes and psychological states; this course may be very useful for those who focus on brain activity. The techniques taught include signal processing, data analysis, and statistics, all within the context of brain signals. A psychophysiologist would greatly appreciate the course's detailed instruction on how to process, clean, analyze, and perform statistics on brain electrical activity, with the help of MATLAB exercises. The course may enable a psychophysiologist to directly use the course material in their research.

See salaries and explore the career path for Psychophysiologist

Neuroscientist

Neuroscientists study the nervous system, including the brain. This course is very helpful for learning analysis techniques applied to brain signals. The course teaches core concepts including signal processing, data analysis, and statistics, all within the context of neural data. A neuroscientist would greatly benefit from the course's focus on processing, cleaning, analyzing, and performing statistics on brain electrical activity. Further, the course may help a scientist understand how to analyze neural datasets, which is critical for advancing neuroscientific research.

See salaries and explore the career path for Neuroscientist

Neuroimaging Analyst

Neuroimaging analysts process and analyze brain images and signals, often using computational tools. This course will be very helpful, as it teaches the basics of analyzing brain signals. The course provides detailed instruction in signal processing, time series analysis, spectral analysis, and statistics, which are key components of the work of a neuroimaging analyst. The hands-on MATLAB exercises will provide neuroimaging analysts with the practical skills needed to work with complex datasets. This course may help a neuroimaging analyst to strengthen their analytical skills.

See salaries and explore the career path for Neuroimaging Analyst

Signal Processing Engineer

Signal processing engineers develop algorithms and systems to analyze and manipulate signals. This course may be very useful for those who want to specialize in neural signals. The course's strong focus on neural signal processing, including time-series analysis, spectral analysis, and data cleaning, directly aligns with the duties of a signal processing engineer. Furthermore, the course provides extensive training in MATLAB, which is an important tool for designing and testing signal processing algorithms. For signal processing engineers, this course may be an asset in building a solid foundation in processing and analyzing complex biological signals.

See salaries and explore the career path for Signal Processing Engineer

Research Scientist

A research scientist often investigates complex datasets, and this course may be useful for those working with neural data. This role involves analyzing time-series data, applying statistical methods, and understanding spectral analysis, all of which are emphasized in the course. The course provides a deep dive into neural signal processing, including hands-on experience with MATLAB, which will be valuable for a research scientist working on brain data. The course may help a researcher build a foundation in data analysis techniques, which are essential for interpreting experimental results and developing new research directions.

See salaries and explore the career path for Research Scientist

Biomedical Engineer

Biomedical engineers design and develop medical devices and systems, including those that interact with neural signals. This course will be useful for biomedical engineers who want to focus on brain-computer interfaces. The course's coverage of signal processing, time series analysis, and spectral analysis is particularly relevant. The MATLAB-based exercises provide hands-on experience that a biomedical engineer can use to analyze and interpret neural data. The course may help develop the fundamental skills to work with neural signals, and this can be applied to many biomedical applications.

See salaries and explore the career path for Biomedical Engineer

Academic Researcher

Academic researchers conduct studies in various fields, and this course may be useful for those using neural data. The coursework's emphasis on signal processing, data visualization, spectral analysis, and statistical methods is directly applicable to research. The course's instruction in data analysis and the use of MATLAB are particularly valuable skills for any academic researcher analyzing experimental data. The course helps build a strong foundation in data processing, which can be necessary for rigorous research.

See salaries and explore the career path for Academic Researcher

Data Analyst

Data analysts seek to extract meaningful insights from complex datasets, and this course may be useful for those dealing with time-series data. The course's focus on signal processing, data visualization, spectral analysis, and statistics directly translates to the work of a data analyst. A data analyst would find the course's detailed exploration of time-series analysis and the use of MATLAB for practical exercises particularly beneficial. The course may help develop skills in data cleaning, processing, and statistical analysis, which are crucial for any data analyst.

See salaries and explore the career path for Data Analyst

Data Scientist

Data scientists analyze large datasets to extract actionable insights, which is related to the course's focus on analyzing brain signals. The course's training in time series analysis, signal processing, and statistical methods is directly applicable to the work of a data scientist, particularly one working with complex time-series or biosignals. The course's use of MATLAB for hands-on work can be an asset for data scientists who need to manipulate data. The course may help a data scientist develop skills in data cleaning, analysis, and model building.

See salaries and explore the career path for Data Scientist

Quantitative Researcher

Quantitative researchers develop and apply mathematical and statistical methods to analyze data, and this course may be useful for those who work with time series data. The course's focus on signal processing, time series analysis, and statistical methods using MATLAB can be quite helpful for a quantitative researcher. The course uses brain data to teach many general principles of data analysis. The course may help quantitative researchers use these time series analysis skills in a wider set of applications.

See salaries and explore the career path for Quantitative Researcher

Bioinformatics Analyst

Bioinformatics analysts examine biological data using computational tools. This course may be useful to those who focus on neural data. The course's curriculum covers signal processing, time-series analysis, and statistical methods, all of which are important in bioinformatics. A bioinformatics analyst will appreciate the course's hands-on approach, using MATLAB to tackle real-world problems involving brain electrical activity. The course may help prepare bioinformaticians to understand and process complex biological signals, making them more effective in their research and analysis.

See salaries and explore the career path for Bioinformatics Analyst

Statistical Modeler

Statistical modelers create models to explain and predict phenomena, and this course may be useful for those who apply these techniques to time series data. The course’s exploration of time series analysis, statistical methods, and signal processing directly aligns with the core duties of a statistical modeler. The course using MATLAB will help a statistical modeler apply these methods. The course may help a statistical modeler to use statistical models in data analysis.

See salaries and explore the career path for Statistical Modeler

Computational Biologist

Computational biologists develop and apply computational methods to biological problems, and this course may be helpful for those who wish to specialize in neural data. The course's emphasis on signal processing, time-series analysis, and statistical analysis is directly relevant to a computational biologist. Further, the course uses MATLAB, which can be helpful in performing computational analysis. The course may strengthen the mathematical foundation required for computational biology.

See salaries and explore the career path for Computational Biologist

Quantitative Analyst

Quantitative analysts develop and implement mathematical and statistical models for financial markets; this course may be useful for quantitative analysts working with time-series data. The course's training in time series analysis, spectral analysis, and statistics is applicable to modeling financial time series. The course's MATLAB exercises and hands-on work allow a quantitative analyst to build models. The course may assist in understanding how to clean, process, and analyze time series data.

See salaries and explore the career path for Quantitative Analyst

Machine Learning Engineer

Machine learning engineers apply machine learning algorithms to solve practical problems, and this course may be useful for those who work with time-series data. The course's curriculum on signal processing, time series analysis, and statistics helps build a foundation for understanding the complex patterns in brain signals. Further, the course provides hands-on experience with MATLAB, an important tool for development and prototyping. The course may help a machine learning engineer develop skills to analyze and process time-series data, and this is critical for advanced machine learning model development.

See salaries and explore the career path for Machine Learning Engineer

Complete neural signal processing and analysis

Zero to hero

What's inside

Learning objectives

Syllabus

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Reviews summary

In-depth neural signal processing in matlab

Activities

Career center

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