On ensemble learning for mental workload classification

Research output: Chapter in Book/Report/Conference proceedingConference contribution book


The ability to determine a subject’s Mental Work Load (MWL) has a wide range of significant applications within modern working environments. In recent years, techniques such as Electroencephalography (EEG) have come to the forefront of MWL monitoring by extracting signals from the brain that correlate strongly to the workload of a subject. To effectively classify the MWL of a subject via their EEG data, prior works have employed machine and deep learning models. These studies have primarily utilised single-learner models to perform MWL classification. However, given the significance of accurately detecting a subject’s MWL for use in practical applications, steps should be taken to assess how we can increase the accuracy of these systems so that they are robust enough for use in real-world scenarios. Therefore, in this study, we investigate if the use of state-of-the-art ensemble learning strategies can improve performance over individual models. As such, we apply Bagging and Stacking ensemble techniques to the STEW dataset to classify “low”, “medium”, and “high” workload levels using EEG data. We also explore how different model compositions impact performance by modifying the type and quantity of models within each ensemble. The results from this study highlight that ensemble networks are capable of improving upon the accuracy of all their individual learner counterparts whilst reducing the variance of predictions, with our highest scoring model being a stacking BLSTM consisting of 8 learners, which achieved a classification accuracy of 97%.
Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Data Science
Subtitle of host publication9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part II
EditorsGiuseppe Nicosia, Varun Ojha, Emanuele La Malfa, Gabriele La Malfa, Panos M. Pardalos, Renato Umeton
Place of PublicationCham
Number of pages15
ISBN (Electronic)9783031539664
ISBN (Print)9783031539657
Publication statusPublished - 15 Feb 2024
EventThird Symposium on Artificial Intelligence and Neuroscience - Grasmere, United Kingdom
Duration: 22 Sept 202326 Sept 2023

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceThird Symposium on Artificial Intelligence and Neuroscience
Abbreviated titleACAIN 2023
Country/TerritoryUnited Kingdom


  • EEG
  • mental workload
  • classification
  • deep learning
  • ensembles


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