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How to inspect time-frequency results
 
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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
Broad overview of EEG data analysis analysis
 
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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
Morlet wavelets in time and in frequency
 
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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
Sine waves in time and in frequency
 
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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
Instantaneous frequency (frequency sliding)
 
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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
Introduction to time series analysis lecturelets
 
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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
Introduction to statistics
 
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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
Short-time Fourier transform
 
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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
Multitaper
 
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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
Fourier transform frequencies and zero-padding
 
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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
Mean-smooth a time series
 
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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
Linear vs. logarithmic time-frequency plots
 
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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
Independent components analysis for removing artifacts
 
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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
The three most important equations in neural time series analyses
 
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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
EEG data and indexing in Matlab
 
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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
Convolution in the time domain
 
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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
Convolution via frequency domain multiplication
 
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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
The Hilbert transform
 
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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
Power-based connectivity analyses
 
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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
Overview of time-domain analyses
 
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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
Stationarity and effects of violations
 
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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 discrete-time Fourier transform
 
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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
Complex sine waves and interpreting Fourier coefficients
 
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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
Spectral Analysis with MATLAB
 
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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 analyses
 
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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
Total vs. phase-locked vs. non-phase-locked power
 
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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
Topographical plots
 
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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
Filter, epoch, baseline subtraction, referencing
 
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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 (a.k.a. inner) product
 
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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
Band-pass filtering and the filter-Hilbert method
 
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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
Euler's formula and extracting power and phase
 
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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
Post-analysis temporal downsampling
 
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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
Inter-trial phase clustering
 
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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
Frequency resolution of wavelet convolution
 
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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
Intro to connectivity, volume conduction, and time- vs. trial-based connectivity
 
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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
Multiclass Epileptic Seizure classification using time frequency analysis of EEG signals
 
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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
Effects of Morlet wavelet parameters on results
 
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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
Adi cohen - class
 
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Adi cohen stupida
Views: 49 shira attiach
Curiosityness Interview with Neil Steven Cohen of EMF Safety Zone
 
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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
Effects of signal nonstationarities on the Fourier power spectrum
 
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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
Holger Rauhut: Compressive sensing with time-frequency structured random matrices
 
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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)
Trial rejection
 
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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
Power-law scaling and the need for normalization
 
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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
Examples of Fourier transform applications
 
09:20
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
Albert Cohen: The joy and pain of wavelets in numerical simulation
 
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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 Recording during the thematic meeting: ''30 years of wavelets: impact and future'' the January 23, 2015 at the Centre International de Rencontres Mathématiques (Marseille, France)
Decibel and percent change power normalizations
 
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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
MULTICLASS EPILEPTIC SEIZURE CLASSIFICATION FOR EEG SIGNALS
 
06:21
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.
Ivan Cohen - Fifty shades of distortion (ADC'17)
 
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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
Matthieu Kowalski: Time-frequency frames and applications to audio analysis - Part 2
 
01:14:29
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 Time-frequency (or Gabor) frames are constructed from time- and frequency shifts of one (or several) basic analysis window and thus carry a very particular structure. On the other hand, due to their close relation to standard signal processing tools such as the short-time Fourier transform, but also local cosine bases or lapped transforms, in the past years time-frequency frames have increasingly been applied to solve problems in audio signal processing. In this course, we will introduce the basic concepts of time-frequency frames, keeping their connection to audio applications as a guide-line. We will show how standard mathematical tools such as the Walnut representations can be used to obtain convenient reconstruction methods and also generalizations such the non-stationary Gabor transform. Applications such as the realization of an invertible constant-Q transform will be presented. Finally, we will introduce the basic notions of transform domain modelling, in particular those based on sparsity and structured sparsity, and their applications to denoising, multilayer decomposition and declipping. (Slides in attachment). Recording during the thematic meeting: "Computational harmonic analysis - with applications to signal and image processing" the October 22, 2014 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent
Dr. Paul Cohen: DARPA Program Manager, DARPA BiT Keynote Speaker
 
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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