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    Complete neural signal processing and analysis: Zero to hero

    BY
    Udemy

    Master signal processing and analytics utilizing brain electrical signals with professional education and coding exercises in MATLAB.

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    Mode

    Online

    Fees

    ₹ 4099

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course overview

    Complete neural signal processing and analysis: Zero to hero online certification was developed by Mike X Cohen, a neuroscientist, writer, and lecturer, and is delivered by Udemy, which is designed for participants who want to learn about the electrical signals in the brain. Complete neural signal processing and analysis: Zero to hero online course by Udemy teaches participants to examine brain electrical patterns in an innovative and exciting method.

    Complete neural signal processing and analysis: Zero to hero online classes incorporate 47 hours of video-based lessons along with 14 articles and 14 downloadable resources which explain to the participants about neural signal processing, data visualization, spectral analysis, data analysis, synchronization analyses, and statistics. With this training program, participants will also learn about topics like time-domain analysis, time series analysis, time-frequency analysis, neuroscience, statistics, permutation testing statistics, and many more.

    The highlights

    • Certificate of completion
    • Self-paced course
    • 47 hours of pre-recorded video content
    • 14 articles
    • 14 downloadable resources

    Program offerings

    • Online course
    • Learning resources. 30-day money-back guarantee
    • Unlimited access
    • Accessible on mobile devices and tv

    Course and certificate fees

    Fees information
    ₹ 4,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    Who it is for

    Neurologist Scientist

    What you will learn

    Mathematical skill

    After completing the Complete neural signal processing and analysis: Zero to hero certification course, participants will gain a deeper knowledge of the functionalities of MATLAB for understanding the principles related to neuroscience and neural signals of the brain. Participants will learn about numerous concepts related to neural signal processing and analysis. Participants will learn about several analytical concepts like time series analysis, time-frequency analysis, time-domain analysis, static spectral analysis, synchronization analysis, and multivariate components analysis. Participants will also get an understanding of spectral analysis applications as well as a working knowledge of algebra, statistics, and permutation testing statistics.

    The syllabus

    Introduction

    • A broad introduction to neural time series analysis
    • Neural data science as source sepatation
    • What to expect from this course
    • A quick note about how this went from 2 to 1 course
    • Download this file if you are using Octave (otherwise ignore)

    The basics of neural signal processing

    • Download MATLAB materials for this course
    • Origin, significance, and interpretation of EEG
    • Overview of possible preprocessing steps
    • ICA for data cleaning
    • Signal artifacts (not) to worry about
    • Topographical mapping
    • Overview of time-domain analyses (ERPs)
    • Motivations for rhythm-based analyses
    • Interpreting time-frequency plots
    • The empirical datasets used in this course
    • MATLAB: EEG dataset
    • MATLAB: V1 dataset
    • Where to get more EEG data?
    • Simulating data to understand analysis methods
    • Problem set: introduction and explanation
    • Problem set (1/2): Simulating and visualizing data
    • Problem set (2/2): Simulating and visualizing data
    • Planck, neuron, universe

    Simulating time series signals and noise

    • MATLAB files for this section
    • Why simulate data?
    • Generating white and pink noise
    • The three important equations (sine, Gaussian, Euler's)
    • Generating "chirps" (frequency-modulated signals)
    • Non-stationary narrowband activity via filtered noise
    • Transient oscillation
    • The eeglab EEG structure
    • Project 1-1: Channel-level EEG data
    • Project 1-1: Solutions
    • Projecting dipoles onto EEG electrodes
    • Project 1-2: dipole-level EEG data
    • Project 1-2: Solutions

    Time-domain analyses

    • MATLAB files for this section
    • Event-related potential (ERP)
    • Lowpass filter an ERP
    • Compute the average reference
    • Butterfly plot and topo-variance time series
    • Topography time series
    • Simulate ERPs from two dipoles
    • Project 2-1: Quantify the ERP as peak-mean or peak-to-peak
    • Project 2-1: Solutions
    • Project 2-2: ERP peak latency topoplot
    • Project 2-2: Solutions

    Static spectral analysis

    • Download MATLAB materials for this section
    • Course tangent: self-accountability in online learning
    • Time and frequency domains
    • Sine waves
    • MATLAB: Sine waves and their parameters
    • Complex numbers
    • Euler's formula
    • MATLAB: Complex numbers and Euler's formula
    • The dot product
    • MATLAB: Dot product and sine waves
    • Complex sine waves
    • MATLAB: Complex sine waves
    • The complex dot product
    • MATLAB: The complex dot product
    • Fourier coefficients
    • MATLAB: The discrete-time Fourier transform
    • MATLAB: Fourier coefficients as complex numbers
    • Frequencies in the Fourier transform
    • Positive and negative frequencies
    • Accurate scaling of Fourier coefficients
    • MATLAB: Positive/negative spectrum; amplitude scaling
    • MATLAB: Spectral analysis of resting-state EEG
    • MATLAB: Quantify alpha power over the scalp
    • The perfection of the Fourier transform
    • The inverse Fourier transform
    • MATLAB: Reconstruct a signal via inverse FFT
    • Frequency resolution and zero-padding
    • MATLAB: Frequency resolution and zero-padding
    • Estimation errors and Fourier coefficients
    • Signal nonstationarities
    • MATLAB: Examples of sharp nonstationarities on power spectra
    • MATLAB: Examples of smooth nonstationarities on power spectra
    • Welch's method for smooth spectral decomposition
    • MATLAB: Welch's method on phase-slip data
    • MATLAB: Welch's method on resting-state EEG data
    • MATLAB: Welch's method on V1 dataset
    • Problem set (1/2): Spectral analyses of real and simulated data
    • Problem set (2/2): Spectral analyses of real and simulated data

    More on static spectral analyses

    • MATLAB files for this section
    • Program the Fourier transform from scratch!
    • Program the inverse Fourier transform from scratch!
    • Spectral separation on simulated dipole data
    • FFT of stationary and non-stationary simulated data
    • FFT and Welch's method on EEG resting state data
    • To taper or not to taper?
    • Extracting average power from a frequency band
    • Comparing average spectra vs. spectra of an average
    • Project 3-1: Topography of spectrally separated activity
    • Project 3-1: Solutions
    • Project 3-2: Topography of alpha-theta ratio
    • Project 3-2: Solutions

    Time-frequency analysis

    • Download MATLAB materials for this section
    • Morlet wavelets in time and in frequency
    • MATLAB: Getting to know Morlet wavelets
    • Convolution in the time domain
    • MATLAB: Time-domain convolution
    • Convolution as spectral multiplication
    • MATLAB: The five steps of convolution
    • MATLAB: Convolve real data with a Gaussian
    • MATLAB: Complex Morlet wavelets
    • Complex Morlet wavelet convolution
    • Convolution coding tips
    • MATLAB: Complex Morlet wavelet convolution
    • MATLAB: Convolution with all trials!
    • MATLAB: A full time-frequency power plot!
    • Averaging phase values
    • Inter-trial phase clustering (ITPC/ITC)
    • MATLAB: ITPC
    • Parameters of Morlet wavelet (time-frequency trade-off)
    • MATLAB: Time-frequency trade-off
    • The stationarity assumption of wavelet convolution
    • The "1/f" structure of spectral brain dynamics
    • Baseline normalization of time-frequency power
    • MATLAB: Baseline normalization of TF plots
    • Scale-free dynamics via detrended fluctuation analysis (DFA)
    • MATLAB: detrended fluctuation analysis
    • The filter-Hilbert time-frequency method
    • MATLAB: Filter-Hilbert
    • The short-time Fourier transform (STFFT)
    • MATLAB: STFFT
    • Comparing wavelet, filter-Hilbert, and STFFT
    • The multi-taper method
    • Within-subject, cross-trial regression
    • MATLAB: Cross-trial regression
    • Temporal resolution vs. precision, pre- and post-convolution
    • MATLAB: Downsampling time-frequency results
    • MATLAB: Linear vs. logarithmic frequency scaling
    • Separating phase-locked and non-phase-locked activity
    • MATLAB: Total, non-phase-locked, and phase-locked power
    • Edge effects, buffer zones, and data epoch length
    • Problem set (1/3): Time-frequency analysis
    • Problem set (2/3): Time-frequency analysis
    • Problem set (3/3): Time-frequency analysis

    More on time-frequency analysis

    • MATLAB files for this section
    • Create a family of complex Morlet wavelets
    • Create a time-frequency plot of a nonlinear chirp
    • Compare wavelet-derived spectrum and FFT
    • Wavelet convolution of close frequencies
    • Time-frequency power of multitrial EEG activity
    • Baseline normalize power with dB and % change
    • Exploring wavelet parameters in real data
    • Exploring wavelet parameters in simulated data
    • Inter-trial phase clustering before vs. after removing ERP
    • Downsampling time-frequency power
    • Visualize time-frequency power from all channels
    • Instantaneous frequency in simulated data
    • Instantaneous frequency in real data
    • Project 4-1: Phase-locked, non-phase-locked, and total power
    • Project 4-1: Solutions
    • Narrowband filtering and the Hilbert transform
    • Project 4-2: Time-frequency power plot via filter-Hilbert
    • Project 4-2: Solutions

    Synchronization analyses

    • Download MATLAB materials for this section
    • Four things to keep in mind about connectivity
    • Volume conduction and what to do about it
    • Intuition about phase synchronization
    • Inter-site phase clustering (ISPC)
    • MATLAB: ISPC
    • Surface Laplacian for connectivity analyses
    • MATLAB: Laplacian in simulated data
    • MATLAB: Laplacian in real EEG data
    • Phase-lag-based connectivity
    • MATLAB: phase-lag index
    • When to use phase-lag vs. phase-clustering measures
    • MATLAB: Phase synchronization in voltage and Laplacian data
    • Connectivity over time vs. over trials
    • MATLAB: Connectivity over time vs. over trials
    • MATLAB: Simulating data to test connectivity methods
    • Two methods of power-based connectivity
    • Granger causality (prediction)
    • MATLAB: Granger causality
    • "Hubness" from graph theory
    • MATLAB: Connectivity hubs
    • When to use which connectivity method?
    • Problem set (1/2): Pairwise synchronization
    • Problem set (2/2): Pairwise synchronization

    More on synchronization analyses

    • MATLAB files for this section
    • Synchronization in simulated noisy oscillators
    • Spurious connectivity in narrowband noise
    • Phase synchronization matrices in multitrial data
    • Power time series correlations
    • Power correlations over trials
    • Scalp Laplacian for electrode-level connectivity
    • All-to-all synchronization and "hubness" (graph theory)
    • Phase-lag index
    • Project 5-1: ISPC and PLI, with and without Laplacian
    • Project 5-1: Solutions
    • Project 5-2: Seeded phase vs. power coupling
    • Project 5-2: Solutions

    Permutation-based statistics

    • Download MATLAB materials for this section
    • Introduction: The basis of statistics, necessity, and levels
    • Parametric vs. nonparametric statistics
    • Permutation-based statistics
    • MATLAB: Permutation testing and shuffling
    • MATLAB: Permutation testing in real data
    • Multiple comparisons and limitations of Bonferroni method
    • Cluster-based multiple comparisons correction
    • MATLAB: Cluster correction
    • Extreme pixel-based multiple comparisons correction
    • MATLAB: Extreme pixel correction
    • Illustrating statistical significance in plots
    • Subject- vs. group-level analyses
    • Error bars and guessing significance
    • Three approaches for group-level statistics
    • MATLAB: Extracting features for group analyses
    • Circular inference ("double-dipping")

    More on permutation testing statistics

    • MATLAB files for this section
    • Permutation testing for one variable and two groups
    • Meta-permutation test for increased stability
    • Permutation testing in simulated time series
    • Permutation testing for cluster correction in simulated data
    • Permutation testing and cluster correction in real EEG data
    • Project 7-1: Effects of noise smoothness on cluster correction
    • Project 7-1: Solutions
    • Project 7-2: Simulate time-frequency data for statistical testing
    • Project 7-2: Solutions

    Multivariate components analysis

    • MATLAB files for this section
    • Background knowledge for this section
    • Simulate multicomponent EEG data
    • Create covariance matrices based on time and on frequency
    • Principal components analysis (PCA) of simulated data
    • Time-based GED for source-separation in simulated data
    • Frequency-based GED for source-separation in simulated data
    • Project 6-1: GED for interacting alpha sources
    • Project 6-1: Solutions

    Bonus section

    • Bonus lecture

    Instructors

    Mr Mike X Cohen

    Mr Mike X Cohen
    Associate Professor
    Freelancer

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