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fNIRS-mental-workload-classifiers

Code for training, evaluating, and visualizing performance of mental workload classification using fNIRS BCI sensors

For users who want to directly try our data on their own pipeline, we provide a simple demo of how to load the data and convert it to standard pytorch dataloader. Please see DataLoader_Demo.ipynb.

Setup

Download dataset

Please visit our website https://tufts-hci-lab.github.io/code_and_datasets/fNIRS2MW.html and download the data. Extract the slide window data into data/slide_window_data folder.

Install Anaconda

Follow the instructions here: https://conda.io/projects/conda/en/latest/user-guide/install/index.html

Create environment

conda env create -f environment.yml

Running experiments

Code for runing each experiment in the paper are located in their own folders:

SelectWindowSize: optimal window size experiments using Random Forest and Logistic Regression

subject_specific_models: subject-speicific models using DeepConvNet/EEGNet/Logistic Regression and Random Forest with the selected window size of 30sec

generic_model: generic-models using DeepConvNet/EEGNet/Logistic Regression and Random Forest with the selected window size of 30sec. 3 scenarios of the generic pool size are experimented (64, 16, 4)

generic_finetuning_models: finetuning the DeepConvNet and EEGNet from corresponding checkpoint of the 64-subject generic pool models with the selected window size of 30sec.

domain_adaptation: utilizing CORAL with Logistic Regression and Random Forest with the selected window size of 30sec. 1 scenario is experimented (generic pool size of 64 subjects and utilizing the target subject's full train set for domain adaptation)

subgroup_analysis: training a generic model solely from subjects of one subgroup's, and see how the performance generalize to othe subgroups with the selected window size of 30sec. Two scenario are experimented (training on White and training on Asian) (since these are the two majority groups in our dataset)

The commands for reproducing results in the paper are provided in runs subfolders inside each experiment folders.

Define the environment variable

export YOUR_PATH="paths to this repo" (e.g., '/home/usr/fNIRS-mental-workload-classifiers', then YOUR_PATH = '/home/usr')

Demo

For example, if you want to train subject-specific DeepConvNet. Go to runs within the subject_sepcific_models folder (Please modify the paths in the scripts according to your actual paths)

bash launch_DeepConvNet_150ts.sh run_here

Analysing results

training curves, confusion matrix, checkpoints etc will be automatically saved in the specified directory in the after running the training commands.

Synthesizing hyperparameter search results

we also provide scripts for synthesizing hyperparameter search results synthesizing_results. For example, if you want to analyse results for subject-specific DeepConvNet, go to the corresponding folder within the synthesizing_results/ folder.

(Please modify the paths in the scripts according to your actual paths)

bash launch_DeepConvNet_150ts.sh run_here

This will generate a csv file called hypersearch_summary.csv for each subject.

bash launch_synthesize_all_subjects.sh run_here

This will generate a csv file called AllSubjects_summary.csv for this experiment.