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%{ Epilepsy Laterality project This is the codebase for the project using interictal EEG data to lateralize temporal lobe epilepsy. Here we describe steps to begin with a dataset containing electrode contact-level features and perform the analysis to calculate patient-level asymmetry indices for each feature, and then perform the machine learning algorithms to predict seizure onset zone laterality and generate the figures from the epilepsy laterality paper. To run the analysis, follow these steps: 1) Download the codebase from: https://github.com/penn-cnt/cnt_tle_laterality 2) Download the datasets from: https://upenn.box.com/s/67upxhl9wam135jn99jtjb72mmmz9aip 3) Create a file called "epilepsy_laterality_locs.m" that contains paths to the codebase, the data folder, and the results folder. It should be structured as follows: function locations = epilepsy_laterality_locs % Locations needed for epilepsy laterality project locations.el_data_folder = ***PATH TO THE DATA FOLDER***; locations.el_plots_folder = ***PATH TO THE RESULTS FOLDER (where plots will be output)***; locations.el_script_folder = ***PATH TO THE CODEBASE***;; end Put this file in your path. 4) If you wish to only run the code to generate figures from intermediate results files, then put the three .mat files from the Box results folder into your results path (locations.el_plots_folder). If you wish to re-run the analysis from scratch, this is not necessary. 5) If you wish to only run the code to generate figures from intermediate results files, then edit plots/do_all_results.m so that do_full_pipeline = 0. 6) Then, to run the analysis, navigate to plots/ and run: >> do_all_results If you rerun only the code to generate figures, this took several minutes (on a 2020 MacBook Air with an Apple M1 chip). If you re-run the code to do the full analysis, this took me about 6 hours on a AMD EPYC 7502P 32-Core Processor. Erin Conrad University of Pennsylvania August 2023 [email protected] %}
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