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How to inspect time-frequency results
 
20:16
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: 9397 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: 9040 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: 2266 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: 13824 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: 711 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: 4321 Mike X Cohen
Multitaper
 
19:47
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: 1872 Mike X Cohen
Short-time Fourier transform
 
20:08
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: 5640 Mike X Cohen
Complex sine waves and interpreting Fourier coefficients
 
27:50
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: 2695 Mike X Cohen
Multiclass Epileptic Seizure classification using time frequency analysis of EEG signals
 
03:59
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: 1207 VERILOG COURSE TEAM
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: 5381 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: 4010 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: 1870 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: 7928 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: 22564 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: 1789 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: 1298 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: 6840 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: 2251 Mike X Cohen
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: 3323 JUCE
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: 5976 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: 700 Mike X Cohen
Choosing a "baseline"
 
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Selecting an appropriate baseline period for normalization is not a trivial decision. Watch and learn! This video uses the following MATLAB files: http://mikexcohen.com/lecturelets/whichbaseline/whichbaseline.m http://mikexcohen.com/lecturelets/sampleEEGdata.mat For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/
Views: 1406 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: 5641 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: 1432 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: 1945 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: 3758 Mike X Cohen
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: 1483 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: 8585 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: 2650 Mike X Cohen
Convolution with many trials
 
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So far, all of the analyses have used single-trial data. But most experiments involve many trials. In this video you will learn about performing wavelet convolution over many trials. You will also learn a few Matlab programming tricks that will help make your analysis faster and more elegant. This video uses the following MATLAB files: http://mikexcohen.com/lecturelets/manytrials/manyTrials.m http://mikexcohen.com/lecturelets/sampleEEGdata.mat For more online courses about programming, data analysis, linear algebra, and statistics, see http://sincxpress.com/
Views: 1817 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: 2106 Mike X Cohen
Adi cohen - class
 
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Adi cohen stupida
Views: 51 shira attiach
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: 4365 Mike X Cohen
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: 5215 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: 1846 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: 1268 Mike X Cohen
MFCN: Digital Signal Processing Lecture 2
 
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Links: Matlab documentation on filter design: https://www.mathworks.com/discovery/filter-design.html Recommended Entry-Mid level textbook on neural signals analysis: Analyzing Neural Time Series Data: Theory and Practice – MX Cohen https://www.amazon.com/Analyzing-Neural-Time-Data-Neuropsychology/dp/0262019876/ref=la_B00EWB0HO2_1_1? s=books&ie=UTF8&qid=1486349621&sr=1-1
Views: 95 Burke Rosen
Examples of Fourier transform applications
 
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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: 2156 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)
Design of 2D-DFT for Multiclass Epileptic Seizure Classification and Analysis of EEG Signals
 
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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 (2D-DFT) 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.
Course intro: Understand the Fourier transform and its applications
 
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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: 2037 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: 586 Mike X Cohen
David Nicholson and Yarden Cohen - Neural Networks for Segmentation of Vocalizations
 
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PyData New York City 2017 Slides: https://speakerdeck.com/nickledave/neural-networks-for-segmentation-of-vocalizations Neural networks for speech-to-text avoid dividing speech into segments, such as syllables, but segmenting has important applications. We compare different neural networks for segmentation of vocalizations using the song of songbirds, which we study as neuroscientists. Initial results suggest a bidirectional LSTM-CNN architecture outperforms others in both segmentation and classification.
Views: 533 PyData
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: 2030 Mike X Cohen
Zero-padding in the time domain
 
08:25
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: 827 Mike X Cohen
Bei mir bist du schejn - frequency analysis
 
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This Video shows the song "Bei mir bist du schejn", as performed by the "Budapest Klezmer Band" together with its frequency spectrum. The original video can be found here: http://www.youtube.com/watch?v=ZUVEq6NC7mM If you ever wanted to know what your music really is, if you're interested in the perfect choreography of oszillations that makes you feel happy or sad, you might be interested in antonia: http://qt-apps.org/content/show.php/antonia?content=118179 (I hope you know http://qt-apps.org ?) antonia (A New TOol for maNIpulating Audiodata) is a program that enables you to manipulate audiodata in the time- as well as in the frequency-domain. Actually, it is not much more than a proof-of-concept, but at least it reveals some information about your music that you never might get otherwise. about the video: Time goes from left to right, frequency from bottom to top. The bar in the middle shows the actual position. The frequency-scale is neither linear nor logarithmic, but is the bark-scale, that is supposed to fit best with humans sense for tone-pitch. The scales on both sides give the tone a=440Hz and its upper and lower octaves. If lower tones look a little blurrier, this is due to a mathematical effect that is related to the uncertainty principle (but it is not a quantum mechanical effect). You simply can't calculate both the time and frequency of a tone with arbitrary high accuracy. Since (for this scale) frequency-resolution increases for lower frequencies, time-resolution has to decrease. http://en.wikipedia.org/wiki/Bark_scale http://en.wikipedia.org/wiki/Uncertainty_principle#Uncertainty_theorems_in_harmonic_analysis
Views: 306 hellnonamefree
Denoising EMG signals via TKEO (Teager-Kaiser energy operator)
 
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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: 490 Mike X Cohen
Patrick Flandrin - Drawing sounds, listening to images: The art of time-frequency analysis
 
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CIRMMT Distinguished Lectures in the Science and Technology of Music Patrick Flandrin, CNRS & Ecole Normale Supérieure de Lyon, France 19 April 2012 - Clara Lichtenstein Recital Hall http://www.cirmmt.mcgill.ca/activities/distinguished-lectures https://www.facebook.com/CIRMMT/ APA video citation: Flandrin, P. (2012, October 22). Drawing sounds, listening to images: The art of time-frequency analysis - CIRMMT Distinguished Lectures in the Science and Technology of Music. [Video file]. Retrieved from https://www.youtube.com/watch?v=06TErgm_iZg
Views: 1172 CIRMMT