Bruno Aristimunha

Bruno Aristimunha

Research Engineer
PhD student

Who is Bruno?

I am a Research Scientist at Yneuro (France 🇫🇷) and (Honorary) Research Associate at the University of California, San Diego (USA 🇺🇸). I obtained my PhD in Computer Science from Paris-Saclay University (France 🇫🇷) and Federal University of ABC (Brazil 🇧🇷), advised by Professors Sylvain Chevallier, Marie-Constance Corsi and Raphael Y. de Camargo.

Research Interests

My current research interests include Learning Representation from the time series (Decoding, Generating and Transferring knowledge), Brain-Computer Interfaces, Machine Learning for brain decoding, Benchmark, and Riemannian Geometry (mostly Symmetric Positive Definite Neural Networks - SPDNets).

Open Source Projects

I strongly advocate for open source for reproducible science and community-driven progress, while occasionally working with closed code. I lead the widely used Python libraries Braindecode pypi downloads and MOABB pypi downloads, actively shaping standards and enabling EEG Decoding in both. I also collaborate with related open-source projects like MNE-Python, MONAI, MONAI Generative, SpeechBrain.

You can usually check my current work on GitHub:

My GitHub Stats

Community Involvement

During my PhD, I collaborated with research groups across the US (San Diego, San Francisco, Washington), UK, Ireland, Germany, Italy, Netherlands, Canada (Waterloo, MILA), Brazil (Sao Paulo), and France, resulting in over 16 publications (full/short papers, reports, abstracts) covering diverse aspects of my doctoral research. My publications are available on Google Scholar. I particularly enjoyed the experience of collaborating on self-contained, code-oriented projects 🧠⚙️. For academic cooperation, please contact me via email or LinkedIn.

Regarding community engagement, I organized the Braindecode Code-Sprint during the European summer of 2023, co-organized the Designing Brain-Computer Interfaces from theory to real-life scenarios Workshop at Graz BCI 2024 Conference and I am currently leading the Special Session on Decoding the brain time series at IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2025.

I have served as a reviewer for machine learning conferences and journals, NeurIPS (x2), ICLR, ICML, NeuroImage, Imaging Neuroscience, Journal of Machine Learning Research (JMLR) and Learning from Time Series for Health Workshop@ICLR, ensuring reviews are within my area of expertise.

🗺️ Journey

Three Acts

A research scientist, in motion

Three countries, one through-line: building tools to decode signals from the brain. The arc bends from a 2012 high-school science fair in Mato Grosso do Sul (I was sixteen) to a cotutelle PhD between Paris-Saclay and UFABC, and now a Research Scientist at Yneuro with an honorary affiliation at UC San Diego INC.

Act 02 — Before
2020 — 2026

PhD in Computer Science (cotutelle)

Université Paris-Saclay 🇫🇷 · UFABC 🇧🇷

Cotutelle PhD: Learning Structure In Electroencephalogram Using Deep Learning (Paris-Saclay) / Geração de Representações Compactas de Sinais EEG (UFABC). Advisors: Sylvain Chevallier, Marie-Constance Corsi, Raphael Y. de Camargo. Sandwich period at King's College London with Walter H. L. Pinaya. Funded by INRIA (FR) and CAPES (BR).

  1. 2026 PhD defense
    Cotutelle thesis defended February 2026 — Paris-Saclay & UFABC.
  2. 2024 Geometric Neural Network (JNE)
    Phase-space SPDNet for BCI-EEG decoding — Journal of Neural Engineering, with Carrara, Corsi, Papadopoulo.
  3. 2024 MOABB benchmark study
    Largest EEG-based BCI reproducibility study for open science. With Chevallier, Carrara, Guetschel, et al.
  4. 2024 Euclidean alignment (JNE)
    Systematic evaluation of Euclidean alignment with deep learning for EEG decoding. Junqueira, Aristimunha, Chevallier, de Camargo.
  5. 2024 Alljoined dataset (CVPR-W)
    EEG-to-Image decoding dataset — CVPR 2024 Workshop on Data Curation in Medical Imaging.
  6. 2024 MOABB Zenodo release
    Mother of all BCI Benchmarks — software registry at INRIA, DOI 10.5281/zenodo.
  7. 2023 Synthetic Sleep EEG (NeurIPS DGM4H)
    Latent diffusion models for EEG generation — NeurIPS 2023 DGM4H Workshop (Spotlight).
  8. 2023 Sleep-Energy (IEEE Access)
    Energy optimization for sleep stage scoring. With Bayerlein, Cardoso, Pinaya, de Camargo.
  9. 2023 IVA for Motor Imagery (ICASSP)
    Independent Vector Analysis on EEG-Based Motor Imagery Classification — ICASSP 2023.
  10. 2023 Braindecode registered
    Software registration with INRIA, V1.0 (01/08/2023).
  11. 2023 Braindecode Code-Sprint
    Organized the European 2023 sprint.
  12. 2023 King's College London (sandwich)
    Visiting period under Walter H. L. Pinaya.
  13. 2023 Started Paris-Saclay leg
    Cotutelle PhD enrollment at Paris-Saclay (in addition to UFABC). INRIA scholarship.
  14. 2022 Glasgow / FGV intern
    Data Scientist intern — University of Glasgow & Fundação Getúlio Vargas.
  15. 2021 FGV consultant
    Data Science consultant — IDB-funded urban-data project (Waze car-accident detection in São Paulo). Stack: AWS, SQL, Python, Dash.
  16. 2020 PhD start (UFABC)
    Began PhD in Computer Science at UFABC under Raphael Y. de Camargo. CAPES scholarship.

📄 Curriculum Vitae

Last updated — April 2026

Open Full CV

🧭 Research Overview

Grouped from the CV update on February 28, 2026. Labels [P#] match your CV numbering.

Open standalone figure

📝 Publications (Full List)

  1. Hajhassani, D., Aristimunha, B., Graignic, P-A., Mellot, A., Kusch, L., Delorme, A., Semah, T., Caillet, A. H. From EEG Cleaning to Decoding: The Role of Artifact Rejection in MI-based BCIs. In 2026 34nd European Signal Processing Conference (EUSIPCO). IEEE. SUBMITTED
  2. Guetschel, P., Aristimunha, B., Truong, D., Kokate, K., Tangermann, M., & Delorme, A. (2026). Toward OpenEEG-Bench: A live community-driven benchmark for EEG foundation models. In EUSIPCO 2026. SUBMITTED
  3. Aristimunha, B., Truong, D., Guetschel, P., Shirazi, S. Y., Guyon, I., Franco, A. R., … & Delorme, A. EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding. NeurIPS 2025.
  4. Klepachevskyi, D., Romano, A., Aristimunha, B., Angiolelli, M., Trojsi, F., Bonavita, S., …, Corsi M.-C. & Sorrentino, P. (2024). Magnetoencephalography-based interpretable automated differential diagnosis in neurodegenerative diseases. Heliyon Cells.
  5. Wimpff, M., Aristimunha, B., Chevallier, S. & Yang, B. (2025). Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study. In EMBC 2025. IEEE.
  6. Darvishi-Bayazi, M. J., Ghonia, H., Riachi, R., Aristimunha, B., Khorasani, A., Arefin, M. R., Dumas, G. & Rish, I. (2024). General-Purpose Brain Foundation Models for Time-Series Neuroimaging Data. NeurIPS 2024 Workshop.
  7. Carrara, I., Aristimunha, B., Corsi, M. C., de Camargo, R. Y., Chevallier, S., & Papadopoulo, T. (2024). Geometric Neural Network based on Phase Space for BCI decoding. Journal of Neural Engineering.
  8. Aristimunha, B., Moreau, T., Chevallier, S., Camargo, R. Y., & Corsi, M. C. (2024). What is the best model for decoding neurophysiological signals? Depends on how you evaluate. CNS 2024.
  9. Rodrigues, G., Aristimunha, B., Chevallier, S. & Camargo, R. Y. de (2024). Combining Euclidean Alignment and Data Augmentation for BCI decoding. In EUSIPCO 2024. IEEE.
  10. Xu, J., Aristimunha, B., Feucht, M. E.*, Qian, E., Liu, C., Shahjahan, T., … & Nestor, A. (2024). Alljoined: A dataset for EEG-to-Image decoding. CVPR 2024 Workshop.
  11. Junqueira, B., Aristimunha, B., Chevallier, S., & de Camargo, R. Y. (2024). A systematic evaluation of Euclidean alignment with deep learning for EEG decoding. Journal of Neural Engineering, 21(3), 036038. doi:10.1088/1741-2552/ad4f18
  12. Aristimunha, B., de Camargo, R. Y., Chevallier, S., Lucena, O., Thomas, A. G., Cardoso, M. J., Pinaya, W. L. & Dafflon, J. (2023). Synthetic Sleep EEG Signal Generation using Latent Diffusion Models. NeurIPS 2023 DGM4H Workshop (Spotlight).
  13. Aristimunha, B., de Camargo, R. Y., Pinaya, W. L., Chevallier, S., Gramfort, A., & Rommel, C. (2023). Evaluating the structure of cognitive tasks with transfer learning. NeurIPS 2023 AI for Science Workshop.
  14. Moraes, C. P., Aristimunha, B., Dos Santos, L. H., Pinaya, W. H. L., de Camargo, R. Y., Fantinato, D. G., & Neves, A. (2023). Applying independent vector analysis on EEG-based motor imagery classification. ICASSP 2023. IEEE.
  15. Aristimunha, B., De Camargo, R. Y., Pinaya, W. H. L., Yger, F., Corsi, M. C., & Chevallier, S. (2023). CONCERTO: Coherence & Functional Connectivity Graph Network. Journee CORTICO 2023.
  16. Carrara, I., Aristimunha, B., Chevallier, S., Corsi, M. C., & Papadopoulo, T. (2023). Holographic EEG: multi-view deep learning for BCI. Journee CORTICO 2023.
  17. Aristimunha, B., Bayerlein, A. J., Cardoso, M. J., Pinaya, W. H. L., & De Camargo, R. Y. (2023). Sleep-Energy: An Energy Optimization Method to Sleep Stage Scoring. IEEE Access, 11, 34595-34602.
  18. Chevallier, S., Carrara, I., Aristimunha, B., Guetschel, P., Lopes, B., … & Moreau, T. (2024). The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark. arXiv:2404.15319. Under review at Journal of Neural Engineering.
  19. Aristimunha, B., Pinaya, W. H. L., de Camargo, R. Y., Chevallier, S., Gramfort, A., & Rommel, C. Uncovering and improving the structure of cognitive tasks with transfer learning. Under review at Imaging Neuroscience.
  20. Aristimunha, B., Carrara, I., Guetschel, P., Sedlar, S., Rodrigues, P., Sosulski, J., Narayanan, D., Bjareholt, E., Quentin, B., Schirrmeister, R. T., Kobler, R., Kalunga, E., Darmet, L., Gregoire, C., Abdul Hussain, A., Gatti, R., Goncharenko, V., Thielen, J., Moreau, T., … Chevallier, S. (2024). Mother of all BCI Benchmarks. Zenodo. https://doi.org/10.5281/zenodo.11545401
  21. Aristimunha, B., Tibor, R., Gemein, L., Gramfort, A., Rommel, C., Banville, H., Sliwowskim, M., Wilson, D., Theo gnassou, P., Gtch, P., Lopes, B., Moreau, T., Sedlar, S., Zamboni, M., Paillard, J., Terris, M., Chevallier, S., … Yao, E. (2023). Braindecode. Zenodo. https://braindecode.org
  22. Aristimunha, B., Ju, C., Collas, A., Bouchard, F., Mian, A., Thirion, B., Chevallier, S., & Kobler, R. (2026). SPD Learn: A geometric deep learning Python library for neural decoding through trivialization. Journal of Machine Learning - Open Source Track. https://spdlearn.org SUBMITTED
  23. Aristimunha, B., Dotan, A., Guetschel, P., Truong, D., Kokate, K., Majumdar, A., Shriki, O., Delorme, A. (2026). EEG-DaSh: An Open Data, Tool, and Compute Resource for Machine Learning on Neuroelectromagnetic Data. Journal of Database. https://eegdash.org SUBMITTED

📖 Education

  1. 09/2020 – 02/2026 PhD in Computer Science

    Cotutelle between Université Paris-Saclay 🇫🇷 and UFABC 🇧🇷. Advised by Sylvain Chevallier, Marie-Constance Corsi, and Raphael Y. de Camargo.

  2. 2016 – 2020 Double BSc in Computer Science & Science and Technology

    Center for Mathematics, Computing, and Cognition, Federal University of ABC (UFABC), Brazil 🇧🇷.

💻 Work Experience

  1. 2026 → Research Scientist, Yneuro

    France 🇫🇷 — building tools for EEG decoding and foundation models on neural signals.

  2. 2026 → Honorary Research Associate, UC San Diego (INC)

    Institute for Neural Computation, USA 🇺🇸.

  3. 03/2022 – 06/2022 Data Scientist Intern, University of Glasgow / FGV

    Brazil 🇧🇷.

  4. 03/2021 – 08/2021 Data Scientist Intern, Getúlio Vargas Foundation (FGV)

    Brazil 🇧🇷.

  5. 07/2014 – 12/2015 Research Intern (Computer Vision), Dom Bosco Catholic University

    Brazil 🇧🇷 — INOVISÃO lab during high school. I published two papers :)

👥 Mentorship

I was privileged to work with and mentor a group of outstanding students:

  • Jose Mauricio

    Master student

    Federal University of ABC, Computer Science.

  • Taha Habib

    Undergrad → Master

    Université Paris-Saclay, now a master student.

  • Gustavo H. Rodrigues

    Undergrad → Master

    Universidade de São Paulo (USP), now a master student at USP.

  • Bruna Junqueira

    Undergrad → Master

    USP, now in the Mathématiques, Vision, Apprentissage master at Université Paris-Saclay.

  • Alexandre Janoni

    Undergrad → Industry

    Federal University of ABC, now at Hospital Albert Einstein.