Twenty-four organizers across 14 institutions in 5 countries!
Listed in the exact order of the proposal's author block. Track / role tag on each card maps to the five challenges (EEG-to-IMG, BCI decoding, sleep onset, EMG-to-Text, and Foundation Transfer), the core platform team, the host organisation, or the senior advisory bench.
Research Scientist at Yneuro (France) and Honorary Research Associate at UC San Diego. PhD in Computer Science from Paris-Saclay and Federal University of ABC, advised by Sylvain Chevallier, Marie-Constance Corsi, and Raphael Y. de Camargo. Leads the Braindecode and MOABB libraries. Same lead as the 2025 EEG Challenge. Personal site →
Braindecode
MOABB
Yneuro and Imperial College Bioengineering. PhD work on EMG biomechanics modelling. Core team for the EEG/EMG Foundation Challenge: runs day-to-day platform operations and connects the Yneuro engineering side with the academic track teams.
Research Scientist in the Brain & AI group at Meta FAIR. PhD at Inria Parietal on self-supervised learning for EEG; previously a researcher at InteraXon. Designs the SSL pretraining for the EEG-to-IMG encoder.
SSL
PhD candidate at the Donders Institute. Deep learning for EEG decoding with a focus on transfer learning, self-supervised learning, and foundation models. Core developer of Braindecode and MOABB.
Braindecode
MOABB
CNRS researcher (ENS, detached) leading the Brain & AI team at Meta AI. Studies the brain and computational bases of human intelligence; develops models that decode brain activity from MEG, EEG, electrophysiology, and fMRI.
At Alljoined, assembles large-scale, image-aligned EEG corpora. Owns the dataset side of Track 1: stimulus protocols, alignment, and the public release used as the warm-up split.
Alljoined. Builds the data pipelines and evaluation harness for Track 1, keeping the EEG-to-image retrieval scoring reproducible across labs and submission environments.
Postdoctoral researcher at Inria Bordeaux on the NEARBY project, working on noise- and variability-robust BCIs for out-of-the-lab use. Motor-imagery BCIs, EEG variability, and ML for neural decoding.
NEARBY
PhD candidate at Inria Bordeaux on the PROTEUS project. Active brain-computer interfaces with a focus on understanding and addressing within-user variability, the same question Track 2 evaluates.
PROTEUS
Professor of neuroergonomics & physiological computing at ISAE-SUPAERO. Co-founder & vice-president of the French BCI association; organised the first passive-BCI competition. Adds the operator-state perspective to BCI evaluation.
Toulouse
Research Director at Inria Bordeaux & LaBRI, leading project-team Potioc on Brain-Computer Interfaces. PI of ANR REBEL/PROTEUS and ERC BrainConquest/SPEARS. USERN Prize 2022; Lovelace-Babbage prize 2023.
Potioc
InteraXon. Owns the consumer-wearable side of Track 3: recording protocols, signal-quality monitoring, and the comparison of Muse-grade EEG against clinical sleep-staging baselines.
Senior Research Scientist at InteraXon (makers of the Muse wearable EEG and fNIRS headband). Builds algorithms that turn raw EEG into clinical signals, including automated sleep staging at expert agreement and non-invasive detection of neurological abnormalities.
Muse
Research Fellow at Imperial College London. Works at the boundary of AI and neuroscience, combining mathematical models of physical and biological systems with modern AI representations. Targets clinical translation.
Bioengineering
Senior Research Scientist Manager at Meta Reality Labs, Paris. Works on machine learning for surface-EMG decoding. Previously Research Director at Inria leading the MIND/Parietal team. Statistical ML, signal processing, and biosignal computing.
MNE
Inria Research Scientist at Paris Brain Institute (NERV Lab). Identifies neurophysiological markers of BCI training and develops interpretable AI tools for neurological-disease diagnosis. Reviews Track 2 submissions for clinical and interpretability quality, not just leaderboard score.
Machine-learning infrastructure engineer at Yneuro. Cloud deployment, software engineering, and computational-neuroscience research projects. Owns the AWS submission and ranking pipeline.
Founder & CEO of Yneuro, the host organisation for the EEG/EMG Foundation Challenge. CentraleSupélec / ESPCI Paris-PSL alum, Stanford School of Medicine master's. Yneuro provides infrastructure and operational support for the challenge.
CEO
Assistant Project Scientist at UC San Diego. Led HBN-EEG curation and annotation. Lead Scientist for BIDS extension proposals to EMG and Stimulus. Core member of the HED working group and the EEGLAB development team.
BIDS · EMG
HBN-EEG
Founding director of the Swartz Center at UCSD. Pioneer in EEG analysis and the development of Independent Component Analysis (ICA) for brain-signal decomposition; leader in mobile brain/body imaging (MoBI).
ICA
Director, Research Scientist at Google DeepMind, in detachment from her professorship at Université Paris-Saclay. President of ChaLearn, community lead of Codalab, JMLR action editor, NIPS 2016 program co-chair, NIPS 2017 general co-chair. 2020 BBVA Frontiers in Research Award (with Schölkopf and Vapnik) for SVMs.
Co-developed the Boltzmann machine; foundational work in deep learning. Connects neuroscience and machine learning. Carries forty years of context on what the field has and hasn't already tried.
Full Professor at Université Paris-Saclay, board member of DATAIA/ClusterIA, co-leader of the TAU team. Leads the Codalab/Codabench framework, the platform the competition runs on.
Codabench
Leads the EEGLAB project. Research Director at CNRS, Research Scientist at UCSD. Owns the reproducibility audit at test-freeze, which is what lets any top entry be replayed byte-for-byte from another team's pipeline.
EEGLAB
The shared mailbox is the canonical address for organiser-side questions. Discord is the fastest path for participant chatter and ad-hoc protocol clarifications — same setup that ran the 2025 challenge.