Bruno Aristimunha

Bruno Aristimunha

Research Engineer
PhD student

Who is Bruno?

I am the 4-th-year Computer Science Ph.D. student at the Paris-Saclay University (France 🇫🇷) and Federal University of ABC (Brazil 🇧🇷), happily 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.

🧭 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.
  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.
  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
  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

📖 Educations

  • 09/2020 – 02/2026, Ph.D. IN COMPUTER SCIENCE @Université Paris-Saclay and UFABC.

  • 2016-2020, Double BSc COMPUTER SCIENCE and Science and Technology, at the Center for Mathematics, Computing, and Cognition, Federal University of ABC (UFABC), Brazil.

💻 Work Experience

  • 03/2022 – 06/2022, Data Scientist Intern, University of Glasgow/FGV, Brazil.
  • 03/2021 – 08/2021, Data Scientist internship, Getúlio Vargas Foundation - FGV, Brazil.
  • 07/2014 – 12/2015, Research Intern during High school in Computer Vision, Dom Bosco Catholic University, Brazil. I published two papers :)

Menthorship

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

  • Jose Mauricio Master Student at Federal University of ABC in Computer Science.
  • Taha Habib Undergraduate student at Paris-Saclay University, now master student.
  • Gustavo H Rodrigues Undergraduate student at Universidade de Sao Paulo, now master student at Universidade de Sao Paulo.
  • Bruna Juqueira Undergraduate student at Universidade de Sao Paulo. Now Mathématiques, Vision, Apprentissage master student at Universite Paris-Saclay.
  • Alexandre Janoni Undergraduate student at Federal University of ABC, now working at Hospital Albert Einstein, the best hospital in Latin America.