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| {{PAGE UNDER CONSTRUCTION}}
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| ==Regularized Higher-Order Partial Least Squares Analysis for ECoG Data==
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| By Frederick Campbell and Kelly Geyer, June 2017
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| ; kelly.l.geyer@rice.edu
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|
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| =Introduction=
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| This page contains documentation of our MATLAB package for the tensor factorization algorithm Regularized Higher-Order Partial Least Squares (RHOPLS) and Principal Component Analysis (RHOPCA). These algorithms are specifically designed for electrocorticography (ECoG) data, with dimensions consisting of trials, electrodes, epoch time, and frequency.
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|
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| ====Current capabilities:====
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| # Data pre-processing: multiple methods for scaling data and removing outlier trails
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| # Regularized Higher-Order PCA: Implementation of RHOPCA for ECoG data
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| # Regularized Higher-Order PLS: Implementation of RHOPLS for ECoG data
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| # Parameter tuning for RHOPLS: Tuning function to select best parameters for RHOPLS
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|
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| ====Upcoming capabilities:====
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| # R wrapper functions: Ability to use this package in R
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| # Integration with RAFE: Ability to visualize RHOPCA heatmaps with RAFE tool, as well as SUMA
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| # Parameter tuning for RHOPCA: Tuning function to select best parameters for RHOPCA
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|
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| =Requirements=
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|
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| ====Required Software:====
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| # R
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| # MATLAB
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|
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| ====Obtaining Code====
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| This package is available on the GitLab page https://gitlab.com/klgeyer/rhop. This is currently a private page so contact Kelly Geyer to obtain an invitation to page or current copy of code.
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|
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| ====Configuration====
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| =====Configuration for Mac OSX=====
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| 1. Create terminal command 'matlab' by typing the following lines in the terminal. For example <path to matlab bin> could be "/Applications/MATLAB_R2015a.app/bin"
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| <code>
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| $ export PATH=$PATH:<path to matlab bin>
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| $ source ~/.bash_profile
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| </code>
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| Test this by typing the following, which should return "<path to matlab bin>/matlab"
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| <code>
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| $ which matlab
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| </code>
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|
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| 2. Configure variables for rhopls code by editing the config.yaml file as needed. You can check values with the commands below
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| <code>
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| $ which R
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| $ which Rscript
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| </code>
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|
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| 3. Make R to recognize the command matlab. Run the following terminal commands
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| <code>
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| $ touch ~/.Renviron
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| $ echo "export PATH=\"/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:$PATH\"" >> ~/.Renviron
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| $ touch ~/.Renviron.site
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| $ echo "export PATH=\"/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:$PATH\"" >> ~/.Renviron.site
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| </code>
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| Test this step by running the following command in R
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| <code>
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| > system("matlab -nodesktop")
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| </code>
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|
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| ====Install Package====
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| Install the MATLAB package with the following commands, which are use to include at the beginning of any .m script that uses this package
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| <code>
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| % Install RHOPLS package
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| cd(rhopls_dir);
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| addpath(pwd);
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| savepath;
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| % Install required toolboxes
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| rhopls_setup();
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| </code>
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|
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| =Processing ECoG Data for Analysis=
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| The RHOPLS package provides several methods of processing ECoG data before an analysis. These include several methods of standardization, as well as removal of outlier trials. The function <code>process_patient_data</code> implements these in a user-friendly wrapper. Ultimately it creates the design tensor for a single patient, which is a 4-dimensional tensor with dimensions (trials x electrode x frequency x time). .
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|
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| ====Data Standardization Methods:====
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| # <code>none</code>: When using the <load_patient_data> function this avoids implementing any standardization. However, it is not an option for the <code>standardize_data</code> function
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| # <code>flatten_trials</code>: This function flattens the ECoG tensor by trial, or first dimension, and then centers and scales data according by user options
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| # <code>array_normal</code>: This function centers and scales data using the array normal method, according by user options. The array normal distribution is a higher-order generalization of the multivariate normal distribution. We estimate a mean and covariance assuming the tensor was generated via the array normal distribution and use the estimated parameters to center and scale the tensor.
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|
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| ====Example====
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| In following example, we mean center and scale the flattened tensor to standardize it. Additionally, outlier are removed. Users can also choose to set either center and scale to false.
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| <code>
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| %% Load and process ECoG data
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| % Import and create a design tensor for patient k
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| std_method = 'flatten_trials';
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| center = true;
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| scaled = true;
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| rm_outliers = true;
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| [X, outliers] = process_patient_data(patient_dir, ...
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| 'standardizationMethod', std_method, ...
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| 'center', center, ...
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| 'scaled', scaled, ...
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| 'removeOutliers', rm_outliers);
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| </code>
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|
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| =ECoG Regularized Higher-Order Least Squares Regression=
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| parameter tuning, fit model
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|
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| =ECoG Regularized Higher-Order Principal Component Analysis=
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| parameter tuning, fit model
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|
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| =Results=
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| do analysis here, show plots
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|
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| =References=
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| # Frederick Campbell, "Interpretable Brain Decoding for Electrocorticography Data though Regularized High Order Partial Least Squares." Publication in progress.
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| # Brett W. Bader, Tamara G. Kolda and others. MATLAB Tensor Toolbox Version 2.6, Available online, February 2015. URL: http://www.sandia.gov/~tgkolda/TensorToolbox/.
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