Integrated Spatio-Temporal Deep Clustering (ISTDC) for cognitive workload assessment

Debashis das Chakladar, Partha pratim Roy, Victor Chang

Research output: Contribution to journalArticlepeer-review


Traditional high-dimensional electroencephalography (EEG) features (spectral or temporal) may not always attain satisfactory results in cognitive workload estimation. In contrast, deep representation learning (DRL) transforms high-dimensional data into cluster-friendly low-dimensional feature space. Therefore, this paper proposes an Integrated Spatio-Temporal Deep Clustering (ISTDC) model that uses DRL followed by a clustering method to achieve better clustering performance. The proposed model is illustrated using four Algorithms and Variational Bayesian Gaussian Mixture Model (VBGMM) clustering method. Temporal and spatial Variational Auto Encoder (VAE) models (mentioned in Algorithm 2 and Algorithm 3) learn temporal and spatial latent features from sequence-wise EEG signals and scalp topographical maps using the Long short-term memory and Convolutional Neural Network models. The concatenated spatio-temporal latent feature (mentioned in Algorithm 4) is passed to the VBGMM clustering method to efficiently estimate workload levels of n-back task. For the 0-back vs. 2-back task, the proposed model achieves the maximum mean clustering accuracy of 98.0%, and it improves by 11.0% over the state-of-the-art method. The results also indicate that the proposed multimodal approach outperforms temporal and spatial latent feature-based unimodal models in workload assessment.

Original languageEnglish
Article number105703
JournalBiomedical Signal Processing and Control
Early online date16 Nov 2023
Publication statusPublished - 1 Mar 2024

Bibliographical note

© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (


  • Cognitive workload
  • Deep clustering
  • Electroencephalography
  • Variational bayesian gaussian mixture model


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