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Depression detection and subgrouping by using the active and passive EEG paradigms

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Abstract

Depression, a paramount global health challenge, necessitates an advanced diagnostic approach. This study employs EEG and AI on a psycho-physiological Healthy Brain Network (HBN) database of 100 patients to recognize and subgroup major depressive disorders during diverse cognitive tasks. After denoising EEG signals and extracting 16383 features, the feature space’s dimensionality is reduced using a minimal-redundancy-maximal-relevance technique. Various AI classifiers (SVM, DT, NB, LR) discern depressed patients from normal controls, and hierarchical cluster analysis (HCA) with t-SNE and kernel PCA showcases superior clustering indices in both active and passive tasks. The results highlight the active sequence learning paradigm’s efficacy, achieving 100% accuracy with a decision tree classifier, underscoring its relevance in discerning depression and revealing its influence on cognitive abilities related to memory and learning. This comprehensive approach not only accentuates the active sequence learning paradigm’s exceptional efficacy with two subgroups but also underscores its pivotal role in unraveling the intricate associations between depression, memory, and learning capacities.

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Acknowledgements

The authors gratefully acknowledge the funding provided by HEC (Higher Education Commission) Pakistan for this NRPU project No. 9402.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Lahore Campus Lahore, Pakistan, Lahore, Pakistan

    Sana Yasin, Imran Raza & Syed Asad Hussain

  2. Department of Computer Science, University of Okara, Okara, Pakistan

    Sana Yasin

  3. Université Paris-Est Créteil (UPEC), LISSI, 94400, Vitry sur Seine, France

    Alice Othmani & Bouibauan Mohamed

  4. Ecole Nationale d’Informatique et d’Analyse des Systèmes ENSIAS, Université Mohammed V, BP 713, Agdal Rabat, Maroc

    Bouibauan Mohamed

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  1. Sana Yasin
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  2. Alice Othmani
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  5. Syed Asad Hussain
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Correspondence to Alice Othmani.

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Sana Yasin and Alice Othmani contributed equally to this work.

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Yasin, S., Othmani, A., Mohamed, B. et al. Depression detection and subgrouping by using the active and passive EEG paradigms. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19184-x

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