Beauchamp:TensorECOG

Regularized Higher-Order Partial Least Squares Analysis for ECoG Data

By Frederick Campbell and Kelly Geyer\\ June 21, 2017

Introduction

This page contains documentation of our MATLAB package for the tensor factorization algorithm Regularized Higher-Order Partial Least Squares (RHOPLS). This algorithm is specifically designed for electrocorticography (ECoG) data, with dimensions consisting of electrodes, epoch time, and frequency.

Current capabilities:

1. Data pre-processing: multiple methods for scaling data and removing outlier trails

Upcoming capabilities:

1. RHOPLS algorithm implementation
2. Visualization of final factorization results, along with capabilities to view data during processing. Potential functionality for SUMA visualization

Processing ECoG Data for Analysis

%% PARAMETERS % 1. data_dir: Directory containing folders (labeled as patient initials) % 2. rhopls_dir: RHOPLS toolbox directory % 3. patient: Patient initials, also name of folder containing patient % data. Inside this directory are three files: % '<patient_initials>_TFR_ao_LowFreq.m', '<patient_initials>_TFR_ao.mat', % and '<patient_initials>_trial_labels.mat' data_dir = '/Users/beauchamplab/Documents/kg_stuff/for_kelly'; rhopls_dir = '/Users/beauchamplab/Documents/kg_stuff/for_kelly/open-source-rhopls/rhopls_code'; patient = 'YAK';


%% Set up required paths % Install RHOPLS package cd(rhopls_dir); addpath(pwd); savepath; % Install required toolboxes rhopls_setup(); % Check contents of patient directory patient_dir = fullfile(data_dir, patient);


%% Load and process ECoG data % Import design matrix for patient k % Create the data matrix that we use for patient k % Description of data loaded: This is a 3-dimensional tensor % Dimensions: (trials X label) - Fred's data std_method = 'flatten_trials'; center = true; scaled = true; rm_outliers = true; overwrite = true; verbose = true; [X, trial_labels, metadata] = load_patient_data(patient_dir, ...

                       'standardizationMethod', std_method, ... 
                       'center', center, ...
                       'scaled', scaled, ...
                       'removeOutliers', rm_outliers, ...
                       'overwrite', overwrite, ...
                       'verbose', verbose);

ECoG RHOPLS

parameter tuning, fit model

Results

do analysis here, show plots