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The python codes for the Analyzing Neural Time Series Data, Mike X Cohen (2012) MIT Press

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.ipynb_checkpoints
.ipynb_checkpoints
 
 
NeuralData
NeuralData
 
 
Chapter02 Advantages and Limitations of Time- and Time-Frequency-Domain Analyses.ipynb
Chapter02 Advantages and Limitations of Time- and Time-Frequency-Domain Analyses.ipynb
 
 
Chapter05 Introduction to the Physiological Bases of EEG.ipynb
Chapter05 Introduction to the Physiological Bases of EEG.ipynb
 
 
Chapter06 Practicalities of EEG Measurement and Experiment Design.ipynb
Chapter06 Practicalities of EEG Measurement and Experiment Design.ipynb
 
 
Chapter09 Overview of Time-Domain EEG Analyses.ipynb
Chapter09 Overview of Time-Domain EEG Analyses.ipynb
 
 
Chapter10 The Dot Product and Convolution.ipynb
Chapter10 The Dot Product and Convolution.ipynb
 
 
Chapter11 The Discrete Time Fourier Transform, the FFT, and the Convolution Theorem.ipynb
Chapter11 The Discrete Time Fourier Transform, the FFT, and the Convolution Theorem.ipynb
 
 
Chapter12 Morlet Wavelets and Wavelet Convolution.ipynb
Chapter12 Morlet Wavelets and Wavelet Convolution.ipynb
 
 
Chapter13 Complex Morlet Wavelets and Extracting Power and Phase.ipynb
Chapter13 Complex Morlet Wavelets and Extracting Power and Phase.ipynb
 
 
Chapter14 Bandpass Filtering and the Hilbert Transform.ipynb
Chapter14 Bandpass Filtering and the Hilbert Transform.ipynb
 
 
Chapter15 Short-Time FFT.ipynb
Chapter15 Short-Time FFT.ipynb
 
 
Chapter16 Multitapers.ipynb
Chapter16 Multitapers.ipynb
 
 
Chapter17 Less Commonly Used Time-Frequency Decomposition Methods.ipynb
Chapter17 Less Commonly Used Time-Frequency Decomposition Methods.ipynb
 
 
Chapter18 Time-Frequency Power and Baseline Normalizations.ipynb
Chapter18 Time-Frequency Power and Baseline Normalizations.ipynb
 
 
Chapter19 Intertrial Phase Clustering.ipynb
Chapter19 Intertrial Phase Clustering.ipynb
 
 
Chapter20 Differences among Total, Phase-Locked, and Non-Phase-Locked Power and Intertrial Phase Consistency.ipynb
Chapter20 Differences among Total, Phase-Locked, and Non-Phase-Locked Power and Intertrial Phase Consistency.ipynb
 
 
Chapter25 Introduction to the Various Connectivity Analyses.ipynb
Chapter25 Introduction to the Various Connectivity Analyses.ipynb
 
 
Chapter28 Granger Prediction.ipynb
Chapter28 Granger Prediction.ipynb
 
 
Chapter29 Mutual Information.ipynb
Chapter29 Mutual Information.ipynb
 
 
Chapter30 Cross-Frequency Coupling.ipynb
Chapter30 Cross-Frequency Coupling.ipynb
 
 
Chapter33 Nonparametric Permutation Testing.ipynb
Chapter33 Nonparametric Permutation Testing.ipynb
 
 
README.md
README.md
 
 
environment.md
environment.md
 
 
mutualinformationx.py
mutualinformationx.py
 
 

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AnalyzingNeuralTimeSeries_Python_Implimentation

The python codes for the Analyzing Neural Time Series Data, Mike X Cohen (2012) MIT Press This is the code that accompanies the book. https://www.amazon.com/Analyzing-Neural-Time-Data-Practice/dp/0262019876

The original MATLAB codes by Cohen

https://github.com/mikexcohen/AnalyzingNeuralTimeSeries.git

Sample Neural Data

.mat file, in their native format, are difficut to handle in languages other than MATLAB. To facilitate easier handling with Python's MNE-Python, we converted the original .mat format EEG data into the .vhdr format (Brain Vision)--which is common and readable EEG format for MNE.

Summary:

Analyzing Neural Time Series Data by Mike X Cohen is an extremely educational book to teach you the conceptual, mathematical, and implementational (but sadly only via Matlab programming) bases of time-, time-frequency-, and synchonization-based analyses of MEG, EEG, and LFP recordings from humans and nonhuman animals.

Despite of being a perfect textbook, some might have some difficulties in implementation as it only supports MATLAB--insanely expensive language. Here, I aim to put the available MATLAB code in Python.

Feel free to use my codes to help your study!!

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The python codes for the Analyzing Neural Time Series Data, Mike X Cohen (2012) MIT Press

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eeg mne-python eeg-analysis eeg-signals-processing

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