Saheed Faremi

Index · Publications

Preprints, papers, and citable work.

2026 3 entries

  1. Morteza Akbari, Saheed Akinpelumi Faremi, Luca Longo

    Autoencoder-Based Models for Scalp EEG: A Systematic Review of Architectures, Applications, Latent Representations, Interpretability, and Validation

    OSF Registries (registered systematic-review protocol), 2026

    preprint Available DOI OSF

    eeg · autoencoders · systematic-review · interpretability · reproducibility

    BibTeX ↓ 
    @misc{akbari2026autoencoderbased,
  author = {Morteza Akbari and Saheed Akinpelumi Faremi and Luca Longo},
  title = {Autoencoder-Based Models for Scalp EEG: A Systematic Review of Architectures, Applications, Latent Representations, Interpretability, and Validation},
  howpublished = {OSF Registries (registered systematic-review protocol)},
  year = {2026},
  doi = {10.17605/osf.io/v2w3z},
  url = {https://osf.io/v2w3z}
}

  2. Saheed Faremi, Luca Longo

    Integrating Convolutional Variational Autoencoders and the Gaussian Mixture Model for efficient manifold learning and clustering of spatially preserved EEG topographic maps

    Brain Informatics, 2026

    journal Available DOI Springer PDF

    eeg · microstates · cvae · gaussian-mixture-model · manifold-learning · brain-informatics

    BibTeX ↓ 
    @article{faremi2026integrating,
  author = {Saheed Faremi and Luca Longo},
  title = {Integrating Convolutional Variational Autoencoders and the Gaussian Mixture Model for efficient manifold learning and clustering of spatially preserved EEG topographic maps},
  journal = {Brain Informatics},
  year = {2026},
  doi = {10.1186/s40708-026-00327-9},
  url = {https://link.springer.com/article/10.1186/s40708-026-00327-9}
}

  3. Saheed Faremi, Andrea Visentin, Luca Longo

    Interpretable EEG Microstate Discovery via Variational Deep Embedding: A Systematic Architecture Search with Multi-Quadrant Evaluation

    XAI 2026 (Late-breaking work + Doctoral Consortium track), Fortaleza, Brazil. arXiv preprint., 2026

    conference Available DOI arXiv Code

    eeg · microstates · variational-deep-embedding · conv-vade · lemon-dataset · xai-2026

    BibTeX ↓ 
    @inproceedings{faremi2026interpretable,
  author = {Saheed Faremi and Andrea Visentin and Luca Longo},
  title = {Interpretable EEG Microstate Discovery via Variational Deep Embedding: A Systematic Architecture Search with Multi-Quadrant Evaluation},
  booktitle = {XAI 2026 (Late-breaking work + Doctoral Consortium track), Fortaleza, Brazil. arXiv preprint.},
  year = {2026},
  doi = {10.48550/arXiv.2605.10947},
  url = {https://arxiv.org/abs/2605.10947}
}

2025 1 entry

  1. Saheed Faremi

    Explainable Disentangled Representation Learning of Recurring Brain Activation Patterns via Variational Autoencoders

    XAI World Conference 2025, Doctoral Proposals track, 2025

    workshop

    doctoral-proposal · explainable-ai · eeg · variational-autoencoder · xai-world-conference

    BibTeX ↓ 
    @inproceedings{faremi2025explainable,
  author = {Saheed Faremi},
  title = {Explainable Disentangled Representation Learning of Recurring Brain Activation Patterns via Variational Autoencoders},
  booktitle = {XAI World Conference 2025, Doctoral Proposals track},
  year = {2025}
}

2024 1 entry

  1. Akinpelumi Saheed Faremi, Boluwaji Akinnuwesi, Elliot Mbunge, Petros M. Mashwama, Stephen Fashoto, Polite Zenzo Ncube, John Batani, Shamsudeen Ademola Sanni, Yinusa A. Faremi, Andile Metfula

    Machine Learning Models for Identifying Factors Influencing and Predicting Malaria Among Children Under Five Years in Nigeria

    IEEE ICTAS 2024, 2024

    conference Available DOI ResearchGate

    health-ai · malaria · nigeria · public-health · ieee

    BibTeX ↓ 
    @inproceedings{faremi2024machine,
  author = {Akinpelumi Saheed Faremi and Boluwaji Akinnuwesi and Elliot Mbunge and Petros M. Mashwama and Stephen Fashoto and Polite Zenzo Ncube and John Batani and Shamsudeen Ademola Sanni and Yinusa A. Faremi and Andile Metfula},
  title = {Machine Learning Models for Identifying Factors Influencing and Predicting Malaria Among Children Under Five Years in Nigeria},
  booktitle = {IEEE ICTAS 2024},
  year = {2024},
  doi = {10.1109/ICTAS59620.2024.10507142},
  url = {https://www.researchgate.net/publication/380116058_Machine_Learning_Models_for_Identifying_Factors_Influencing_and_Predicting_Malaria_Among_Children_Under_Five_Years_in_Nigeria}
}