Code for: Perceived Mental Workload Classification Using Intermediate Fusion Multimodal Deep Learning

This repository contains all code -from data collection to perceived mental workload classification- used in the "Perceived Mental Workload Classification using Intermediate Fusion Multimodal Deep Learning" research and serves as supplementary material. A clear README file is provided, please refer to that for information about the individual scripts and their usage.

A lot of research has been done on detection of mental workload using various bio-signals. Recently, deep learning has allowed for novel methods and results. A plethora of modalities have proven to be valuable in this task, yet studies currently often only use a single modality to classify mental workload. The goal of this research was to classify perceived mental workload using a deep neural network that flexibly makes use of multiple modalities.

A dataset on which these scripts was also made publicly available under the following DOI: 10.4121/12932801
This dataset contains data collected during research into mental workload (MWL) detection using deep learning. It is being made public as supplementary data for publications, as well as for reuse in research that seeks to classify MWL using multimodal physiological data. The goal of this repository and dataset is to serve as a testing ground for the creation of deep neural networks that can classify MWL using multimodal physiological data.

The data in this dataset was collected in the Behavioural, Management, and Social Sciences Lab, University of Twente, Enschede, The Netherlands in June/July 2020.