On EEG preprocessing role in deep learning effectiveness for mental workload classification

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Kingphai, Kunjira and Moshfeghi, Yashar; Longo, Luca and Leva, Maria Chiara, eds. (2021) On EEG preprocessing role in deep learning effectiveness for mental workload classification. In: Human Mental Workload. Communications in Computer and Information Science . Springer, Virtual, Online, pp. 81-98. ISBN 9783030914073 (https://doi.org/10.1007/978-3-030-91408-0_6)

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Abstract

A high mental workload level could significantly contribute to mental fatigue, decreased performance, or long-term health problems [14]. Recently, deep learning models have been trained on Electroencephalogram (EEG) signals to detect users' mental workload. While such approaches show promising results, they either ignore the noise element inherent in the EEG signals or apply a random set of preprocessing techniques to reduce the noise. Such a lack of uniform preprocessing techniques in cleaning the EEG signals would not allow the comparison of the effectiveness of deep learning models across different studies even when they use the data collected from the same experiment. Therefore, in this study, we aim to investigate the effect of preprocessing techniques defined by neuroscientists in the effectiveness of deep learning models. To do so, we focused on the preprocessing techniques that can be automated and do not need any human intervention, namely a high-pass filter, the ADJUST algorithm, and a re-referencing. Using a publicly available mental workload dataset, STEW, we investigate the effect of these preprocessing techniques in three state-of-the-art deep learning models named Stacked LSTM, BLSTM, and BLSTM-LSTM. Our results show that ADJUST has the most significant effect on the performance of our models compare to other steps. Our findings also show that EEG signals that were prepossessed using the high-pass filter, ADJUST algorithm and re-referencing provided the highest classification performance across the investigated deep learning models. We believe this paper provides an important step towards defining a uniform methodological framework for using deep learning models on EEG signals.

ORCID iDs

Kingphai, Kunjira and Moshfeghi, Yashar ORCID logoORCID: https://orcid.org/0000-0003-4186-1088; Longo, Luca and Leva, Maria Chiara
  • Item type:Book Section
    ID code:96295
    Dates:
    Date
    Event
    23 November 2021
    Published
    Notes:Copyright © 2021 Springer-Verlag. This version of the paper has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at https://doi.org/10.1007/978-3-030-91408-0_6
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:18 May 2026 10:31
    Last modified:04 Jun 2026 02:02
    URI:https://strathprints.strath.ac.uk/id/eprint/96295
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