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.

I am currently seeking job/intership opportunities in the industry. If there are suitable positions available, please feel free to reach out. Thank you!

đź“ť Publications

  • 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, The paper has been accepted.

  • 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 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1-7). IEEE.

  • 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. Workshop on Time Series in the Age of Large Models @ NeurIPS 2024.

  • 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.

  • Aristimunha, B., Moreau T., Chevallier, S, Camargo, R. Y., & Corsi, M. C. What is the best model for decoding neurophysiological signals? Depends on how you evaluate. In 33rd Annual Computational Neuroscience Meeting*CNS 2024.

  • Rodrigues, G., Aristimunha, B., Chevallier, S. & Camargo, R. Y. de (2024). Combining Euclidean Alignment and Data Augmentation for BCI decoding. In 2024 32nd European Signal Processing Conference (EUSIPCO) (pp. 1-6). IEEE.

  • Xu, J., Aristimunha, B., Feucht, M. E.*, Qian, E., Liu, C., Shahjahan, T., … & Nestor, A. (2024). Alljoined–A dataset for EEG-to-Image decoding. Workshop Data Curation and Augmentation in Medical Imaging at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1–9.

  • 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

  • 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. In Deep Generative Models for Health Workshop NeurIPS 2023. SPOTLIGHT

  • 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. In AI for Science Workshop NeurIPS 2023.

  • Moraes, C. P., Aristimunha, B., Dos Santos, L. H., Pinaya, W. H. L., de Camargo, R. Y., Fantinato, D. G., & Neves, A. (2023, June). Applying independent vector analysis on eeg-based motor imagery classification. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.

  • Aristimunha, B., De Camargo, R. Y., Pinaya, W. H. L., Yger, F., Corsi, M. C., & Chevallier, S. (2023). CONCERTO: Coherence & Functional Connectivity Graph Network. In JournĂ©e CORTICO 2023.

  • Carrara, I., Aristimunha, B., Chevallier, S., Corsi, M. C., & Papadopoulo, T. (2023). Holographic EEG: multi-view deep learning for BCI. In JournĂ©e CORTICO 2023.

  • 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.

  • 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 preprint arXiv:2404.15319. The manuscript is under review in the Journal of Neural Engineering.

  • B Aristimunha, WHL Pinaya, RY de Camargo, Sylvain Chevallier, Alexandre Gramfort, CĂ©dric Rommel. Uncovering and improving the structure of cognitive tasks with transfer learning. Manuscript under review at Imaging NeuroScience.

Open Software:

  • Mother of all BCI Benchmarks **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., Zenodo. https://doi.org/10.5281/zenodo.11545401, 2024

  • Braindecode Aristimunha B., Tibor, R., Gemein L., Gramfort, A., Rommel, C., Banville H., Sliwowskim M., Wilson, D., Theo gnassou, Pierre Gtch, Bruna Lopes, Thomas Moreau, Sara Sedlar, Marco Zamboni, Joseph Paillard, Matthieu Terris, Sylvain Chevallier, … Edward Yao., Zenodo., 2023

đź“– Educations

  • 09/2020 – 02/2026, Ph.D. IN COMPUTER SCIENCE @UniversitĂ© 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.

đź’» Work Experience

  • 03/2022 – 06/2022, Data Scientist Intern, University of Glasgow/FGV, Brazil.
  • 03/2021 – 08/2021, Data Scientist internship, GetĂşlio Vargas Foundation - FGV, Brazil.
  • 07/2014 – 12/2015, Research Intern in Computer Vision, Dom Bosco Catholic University, Brazil.

Menthorship

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