If you are unsure of how to look at time-frequency results, this video has the 5-step plan that you need! It also discusses whether time-frequency features can be interpreted as "oscillations."
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 8313
Mike X Cohen

This lecture is a very broad introduction to the most commonly used data analyses in cognitive electrophysiology. There is no math, no Matlab, and no data to download.
For more information about MATLAB programming:
https://www.udemy.com/matlab-programming-mxc/?couponCode=MXC-MATLAB10
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 11892
Mike X Cohen

Convolution requires two time series: The data and the kernel. The data is what you already have (EEG/MEG/LFP/etc); here you will learn about the most awesomest kernel for time-frequency decomposition of neural time series data: The Morlet wavelet.
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/morlet/morletWavelet.m
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 7637
Mike X Cohen

This video provides a deeper introduction to sine waves. This lecture plus the next lecture (dot-product) are essential for understanding how the Fourier transform works.
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/sinewave/sinewave.m
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 3716
Mike X Cohen

Although most time-frequency analysis methods assume frequency stationarity, there are several ways to measure the time-varying changes in dominant frequency within a restricted range (e.g., within the alpha band). Instantaneous frequency ("frequency sliding") is one of those methods.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/freqslide/freqslide.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1876
Mike X Cohen

A short introduction to these lectures, what you will get out of them, and how best to learn from them. And you'll see a picture of the disembodied voice behind all the lectures.
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 5257
Mike X Cohen

This lecturelet will introduce you to the series on statistical analyses of time-frequency data.
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1124
Mike X Cohen

Here you will learn about the short-time Fourier transform (STFFT; the extra "F" is for "fast"), which is another method for time-frequency analysis.
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/stfft/stfft.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
A full-length course on the Fourier transform using MATLAB and Python programming can be found here: https://www.udemy.com/fourier-transform-mxc/?couponCode=MXC-FOURIER10
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 4826
Mike X Cohen

The final time-frequency analysis method shown here is the multitaper method. It is an extention of the STFFT that can be useful in low-SNR situations.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/multitaper/multitaper.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1583
Mike X Cohen

The final thing to know about the Fourier transform is how to convert unit-indices to frequencies in Hz. You will also learn about frequency resolution and how to increase resolution by zero-padding. Patrick McGoohan makes another guest appearance.
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/frequencies/frequencies.m
A full-length course on the Fourier transform using MATLAB and Python programming can be found here: https://www.udemy.com/fourier-transform-mxc/?couponCode=MXC-FOURIER10
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 3582
Mike X Cohen

This is part of an online course on beginner/intermediate applied signal processing, which presents theory and implementation in MATLAB and Python. The course is designed for people interested in applying signal processing methods to applications in time series analysis.
More info here: https://www.udemy.com/signal-processing/?couponCode=MXC-DISC4ALL

Views: 201
Mike X Cohen

This video has a quick discussion on whether you should use logarithmically or linearly spaced time-frequency plots. It also contains a few hints and tricks about using imagesc and contourf functions.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/loglinTF/loglinTF.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 601
Mike X Cohen

This lecturelet will illustrate one method of identifying independent components for removal.
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 7487
Mike X Cohen

There are three equations that form the mathematical backbone of nearly all time-frequency-based analyses. If you memorize these equations, and can learn to recognize them embedded inside code and long equations, you will find learning data analysis to be much more intuitive and tangible.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/trinity/threeImportantEquations.m
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1718
Mike X Cohen

This video describes how to identify time/frequency/electrode points in your data, as well as a few tips for Matlab programming and debugging.
The video uses the following files (and also the topoplot function, which is free to download with the eeglab toolbox):
http://mikexcohen.com/lecturelets/indexing/indexing.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
A full-length course on MATLAB programming and debugging can be found here: https://www.udemy.com/matlab-programming-mxc/?couponCode=MXC-MATLAB10
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 5955
Mike X Cohen

Now that you understand the Fourier transform, it's time to start learning about time-frequency analyses. Convolution is one of the best ways to extract time-frequency dynamics from a time series. Convolution can be conceptualized and implemented in the time domain or in the frequency domain. It is important to understand both conceptualizations. We start with the time domain implementation.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/convolutiontime/convolutionTime.m
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 6819
Mike X Cohen

Is time-domain convolution too slow? (Yes it is.) Learn how to do lightning-fast convolution in the frequency domain. This will also help you understand that wavelet convolution is really just filtering.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/convfreqdom/convolutionAsFreqMult.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 4598
Mike X Cohen

In this video you will learn about the Hilbert transform, which can be used to compute the "analytic signal" (a complex time series from which instantaneous power and phase angles can be extracted).
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/hilbert/hilbertX.m
mikexcohen.com/lecturelets/sampleEEGdata.mat
For more information about spectral analysis: https://www.udemy.com/fourier-transform-mxc/?couponCode=MXC-FOURIER10
For more information about MATLAB programming: https://www.udemy.com/matlab-programming-mxc/?couponCode=MXC-MATLAB10
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 19148
Mike X Cohen

This lecturelet will introduce you to two methods of estimating functional connectivity via time-frequency power.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/powerconn/powerconn.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 588
Mike X Cohen

This video introduces the mathematics behind event-related potentials (averaging) and how to compute ERPs in Matlab. You'll also learn how ERPs were computed in the 1950's.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/timedomain/timedomain.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1938
Mike X Cohen

In simplistic introductory tutorials on the Fourier transform, you see examples of perfectly stationary signals ("stationary" means the statistical properties such as mean, variance, etc., remain the same over time). But brain activity is far from stationary! In fact, it is fairly accurate to say that neuroscientists are specifically interested in the brain's non-stationarities (for example, after seeing a visual stimulus or when recalling a specific memory).
In this video you will learn what happens in the frequency domain when the time-domain signal is non-stationary. And to quell suspense: The Fourier transform is always a perfect and useful reconstruction, even when the data violate stationarity.
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/fftstationarity/fftstationarity.m
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1068
Mike X Cohen

The Fourier transform is arguably the most important algorithm in signal processing and communications technology (not to mention neural time series data analysis!). This video provides an in-depth, step-by-step explanation of how the Fourier transform works.
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/fourier/fourier.m
A full-length course on the Fourier transform using MATLAB and Python programming can be found here: https://www.udemy.com/fourier-transform-mxc/?couponCode=MXC-FOURIER10
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 3310
Mike X Cohen

Now that you know the basic mechanics underlying the Fourier transform, it's time to learn about complex numbers, complex sine waves, and how to extract power and phase information from a complex dot product. Don't worry, it's actually not so complex!
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/complexfourier/complexfourier.m
A full-length course on the Fourier transform using MATLAB and Python programming can be found here: https://www.udemy.com/fourier-transform-mxc/?couponCode=MXC-FOURIER10
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 2373
Mike X Cohen

See what's new in the latest release of MATLAB and Simulink: https://goo.gl/3MdQK1
Download a trial: https://goo.gl/PSa78r
MathWorks engineers illustrate techniques of visualizing and analyzing signals across various applications. Using MATLAB and Signal Processing Toolbox functions we show how you can easily perform common signal processing tasks such as data analysis, frequency domain analysis, spectral analysis and time-frequency analysis techniques. This webinar is geared towards scientists / engineers who are not experts in signal processing.
Webinar highlights include:
A practical introduction to frequency domain analysis.
How to use spectral analysis techniques to gain insight into data.
Ways to easily carry out signal measurement tasks.
View example code from this webinar here.
About the Presenter
Kirthi Devleker is the product marketing manager for Signal Processing Toolbox at MathWorks. He holds a MSEE degree from San Jose State University

Views: 32022
MATLAB

Phase-based connectivity methods are the most widely used for estimating frequency-band-specific functional connectivity. This video provides a discussion and illustration of two classes of phase-based connectivity methods (cluster-based and lag-based).
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/phaseconn/phase_connectivity.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1235
Mike X Cohen

Time-frequency power can be decomposed into phase-locked and non-phase-locked components. This is a methodological distinction that helps understand which time-frequency power features are also phase-locked.
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/nonphaselocked/nonphaselocked.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1249
Mike X Cohen

This video introduces how to interpret and create interpolated spatial distributions of electrode activity (a.k.a. topographical maps).
The video uses the following MATLAB code (you will also need the eeglab toolbox, which is free to download).
http://mikexcohen.com/lecturelets/topoplots/topomaps.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1737
Mike X Cohen

This lecture provides a brief overview of EEG preprocessing steps.
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 4791
Mike X Cohen

The dot product is a simple but important algorithm for many signal processing applications, including the Fourier transform and convolution. In this video, you will learn how to compute the dot product, how to interpret the sign of a dot product, and two interpretations (algebraic and geometric) of the dot product.
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1773
Mike X Cohen

The Hilbert transform produces uninterpretable results on broadband data. You will need to narrow-band filter the signal first. This video shows one method of computing an FIR filter and applying it to EEG data. Together with the Hilbert transform, this gives us the filter-Hilbert method.
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/firfilter/firfilter.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 3851
Mike X Cohen

Extracting time-frequency information from the result of complex Morlet wavelet convolution involves reinterpreting Euler's formula (eik), which allows you to extract three important pieces of information from the result of complex Morlet wavelet convolution (power, phase, and the band-pass filtered signal). This lecture is different from the one on complex Fourier coefficients, so don't skip this one!
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/eulersconvolution/eulers_and_convolution.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 2301
Mike X Cohen

Are your time-frequency results matrices too big? Watch this video to learn how to reduce the temporal resolution of your results to match their temporal precision, which can save lots of time and space.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/postanal_ds/postanalysis_ds.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 511
Mike X Cohen

So far you've just been learning about how to deal with the power of the time-frequency results. This video will introduce you to working with phase values, which reflect the timing of the activity relative to the time=0 event (e.g., stimulus onset).
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/itpc/itpc.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1619
Mike X Cohen

This video explains how you can measure 2 Hz activity in 200 ms. It's a clarification of a question that I often get.
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1624
Mike X Cohen

This lecturelet will introduce you to four considerations to keep in mind when performing or evaluating functional connectivity analyses with EEG/LFP data.
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1527
Mike X Cohen

Seizure is a transient abnormal behavior of neurons within one or several neural networks, which limits the patients physical and mental activities. Since conventional time or frequency domain analysis is found inadequate to describe the characteristics of a non-stationary signal, such as electroencephalography (EEG), in this paper, we propose to transform the EEG data using twelve Cohen class kernel functions in order to facilitate the time-frequency analysis. The transformed data thus obtained is exploited to formulate a feature vector consists of modular energy and modular entropy that can better model the time-frequency behavior of the EEG data. The feature vector is fed to an Artiﬁcial Neural Network (ANN) classiﬁer in order to classify epileptic seizure data originating from different parts and state of the brain. A number of simulations is carried out using a benchmark EEG dataset. It is shown that the proposed method is capable of producing greater accuracy in comparison to that obtained by using a state-of-the-art method of epileptic seizure classiﬁcation using the same EEG dataset and classiﬁer.

Views: 1188
VERILOG COURSE TEAM

There is one important parameter of Morlet wavelets, which is the width of the Gaussian (a.k.a. the "number of cycles"). In this video we will explore this parameter and see what effects different parameter values have on the results. I will also provide some advice for when you should use which parameter settings, and how to interpret results of different settings.
This video uses the following MATLAB code:
http://mikexcohen.com/lecturelets/waveletparams/waveletparams.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 4532
Mike X Cohen

PLEASE SUPPORT my EMF Safety Zone Channel!
Any size contribution is appreciated!
https://paypal.me/NeilSCohen?locale.x=en_US
- EMF Meters, Consulting, Resources: https://emf-protection.us
- eBay store for EMF meters, protecting clothing, and related products at the lowest prices: http://www.ebaystores.com/healthyjoyfulsustainableliving
Join me on Twitter for educational updates! https://twitter.com/EMFSafetyZone / @EMFSafetyZone
Here is an excellent and informative EMF interview I did with Travis DeRose of Curiosityness.com.

Views: 1123
EMF Safety Zone

This is part of an online course on foundations and applications of the Fourier transform. The course includes 4+ hours of video lectures, pdf readers, exercises, and solutions.
Each of the video lectures comes with MATLAB code, Python code, and sample datasets for applications. With 3000+ lines of code, this course is also a great way to improve your programming skills.
More info here: https://www.udemy.com/fourier-transform-mxc/?couponCode=MXC-FOURIER10

Views: 295
Mike X Cohen

Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities:
- Chapter markers and keywords to watch the parts of your choice in the video
- Videos enriched with abstracts, bibliographies, Mathematics Subject Classification
- Multi-criteria search by author, title, tags, mathematical area
One of the important "products" of wavelet theory consists in the insight that it is often beneficial to consider sparsity in signal processing applications. In fact, wavelet compression relies on the fact that wavelet expansions of real-world signals and images are usually sparse. Compressive sensing builds on sparsity and tells us that sparse signals (expansions) can be recovered from incomplete linear measurements (samples) efficiently. This finding triggered an enormous research activity in recent years both in signal processing applications as well as their mathematical foundations. The present talk discusses connections of compressive sensing and time-frequency analysis (the sister of wavelet theory). In particular, we give on overview on recent results on compressive sensing with time-frequency structured random matrices.
Recording during the thematic meeting: ''30 years of wavelets: impact and future'' the January 24, 2015 at the Centre International de Rencontres Mathématiques (Marseille, France)

Views: 1187
Centre International de Rencontres Mathématiques

Removing EEG-artifact-laden trials from the data before analyses is an important step in preprocessing. In this lecturelet, you'll see some examples of artifacts in EEG data.
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1291
Mike X Cohen

Time-frequency power decreases nonlinearly with increasing frequency. In this video you will learn about the problems in interpretation and statistics that this "power-law" creates, and in the next video you will learn a few ways to remedy the situation.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/powerlaw/powerLawTF.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 1584
Mike X Cohen

This is part of an online course on foundations and applications of the Fourier transform. The course includes 4+ hours of video lectures, pdf readers, exercises, and solutions.
Each of the video lectures comes with MATLAB code, Python code, and sample datasets for applications. With 3000+ lines of code, this course is also a great way to improve your programming skills.
More info here: https://www.udemy.com/fourier-transform-mxc/?couponCode=MXC-FOURIER10

Views: 1675
Mike X Cohen

Following-up on the previous video, this one introduces two methods for performing baseline normalization.
This video uses the following MATLAB files:
http://mikexcohen.com/lecturelets/dbpct/dbpct.m
http://mikexcohen.com/lecturelets/sampleEEGdata.mat
For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/

Views: 900
Mike X Cohen

This project is designed based on the paper "Multiclass Epileptic Seizure Classification Using Time-Frequency Analysis of EEG Signals".Seizure is a transient abnormal behaviour of neurons within one or several neural networks, which limits the patients physical and mental activities. Since conventional time or frequency domain analysis is found inadequate to describe the characteristics of a non-stationary signal, such as electroencephalography (EEG), in this Matlab design, EEG data is transform using twelve Cohen class kernel functions in order to facilitate the time-frequency analysis. The transformed data thus obtained is exploited to formulate a feature vector consists of modular energy and modular entropy that can better model the time-frequency behaviour of the EEG data. The feature vector is fed to an Artificial Neural Network (ANN) classifier in order to classify epileptic seizure data originating from different parts and state of the brain. The proposed EEG based epileptic seizure classification method consists of some major steps, namely, pre-processing, time-frequency analysis, feature extraction (FFT) and classification. In the classification, we consider five classes of epileptic seizure data, namely Z, O, N, F and S. Several simulations are carried out using a benchmark EEG dataset. number of simulations is carried out using a benchmark EEG dataset. It is shown that the proposed method can produce greater accuracy in comparison to that obtained by using a state-of-the-art method of epileptic seizure classification using the same EEG dataset and classifier
Reference Paper: Multiclass Epileptic Seizure Classification Using Time-Frequency Analysis of EEG Signals
Author’s Name: Partha Pratim Acharjee, and Celia Shahnaz
Source: IEEE
Year:2012
Need design files, send student university identity card along with HOD/Supervisor details by whatsapp @+91 7904568456 or get the source codes by paying the fee.

Views: 26
VCT-MATLAB PROJECTS

Fifty shades of distortion
Ivan Cohen, Freelance Software Developer & Owner, Musical Entropy
‘Distortion’ is a word we hear a lot in audio and DSP areas. Historically, it is associated with "nonlinear distortion" (NLD), and we talk a lot nowadays about the "saturation" of high-gain guitar amplifiers, "fuzz" and "overdride" pedals, audio effects such as dynamics compressors, exciters, tape recorders and transformers simulations... But we use also that word for the phase response of some textbook IIR filters, for "spatial distortion" meaning changes related with multi-channels audio streams. Distortion exists in a lot of contexts with different meanings and origins, like bias distortion, crossover, granular, group delay distortions, bitcrushing, hysteresis, chaos, aliasing, frequency-warping, clipping, slew rate, glitches, inter-peak clipping and even programming bugs ! In this talk, you will listen to a song which has been designed to exhibit around fifty different kinds of distortion, and we will study most of them, to understand better why some of these algorithms feature the so-called "analog warmth". You will learn some basic principles of analog modeling, how to bring some life in your classic waveshapers, using the features of the new DSP module, and you will discover how to code original audio effects based on unknown before kinds of distortion.
Presented at ADC 2017, Code Node, London.
https://juce.com/adc-2017

Views: 2971
JUCE

Dr. Paul Cohen joined DARPA as a program manager in September 2013. His research interests span artificial intelligence and include machine learning, language, vision, semantic technology, data analysis, information theory and education informatics.
Dr. Cohen joined DARPA from the University of Arizona, where he is professor and founding director of the university’s School of Information: Science, Technology and Arts. He has also served as head of the university’s department of computer science.

Views: 923
DARPAtv