Meet the 31 organizers behind the four tracks, datasets, and evaluation. Choose a team below or ask the organizers on Discord.
Research Scientist in the Brain & AI group at Meta FAIR. His research focuses on machine learning for decoding and processing neuroimaging data. Designs the SSL pretraining for the EEG-to-IMG encoder.
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.
PhD candidate at ENS and Meta FAIR. Aligns deep-network and neural-data representations. Co-author of NeuralSet, a Python neuro-AI package, and TRIBEv2, a foundation brain encoder.
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.
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.
Professor of neuroergonomics & physiological computing at ISAE-SUPAERO. Co-founder & vice-president of the French BCI association and organiser of the first passive-BCI competition. Adds the operator-state perspective to BCI evaluation.
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 and Lovelace-Babbage prize 2023.
Postdoctoral researcher at the Integrated Systems Laboratory (IIS), ETH Zürich, in Luca Benini's group. His ETH Zürich PhD focused on robust seizure detection with wearable EEG. He develops biosignal foundation models including LUNA, LuMamba, FEMBA, CeREBrO, and PanLUNA, and deploys them on ultra-low-power edge hardware so AI can run on microwatts. Personal site →
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.
Muse. Senior Research Scientist leading EEG and multimodal foundation-model research on one of the world’s largest wearable EEG datasets, with a focus on large-scale pretraining and generalizable neurophysiological representations.
Senior Research Scientist at Muse, makers of the 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.
Chief Innovation Officer and co-founder of Muse. Leads R&D advancing wearable neurotech with sleep science and AI. His background spans VR/AR, humanistic intelligence, computer vision, and robotics.
Research Science Director 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.
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.
Research scientist at Meta Reality Labs working in AI for neural interfaces. His work mostly revolves around the idea of learning and exploiting data symmetries (invariances and equivariances) to make neural networks more data efficient and robust. Personal website →
Richard (Rick) Warren researches machine learning and neuroscience at Meta Reality Labs. He earned a neuroscience PhD at Columbia University studying how the brain controls complex behavior, with research internships at DeepMind and Meta Reality Labs. Official bio & portrait →
Research Software Engineer on the CTRL team at Meta Reality Labs, with a background in theoretical physics and neuroscience. His research has used mathematical models and numerical simulations to study neural circuits and biologically plausible learning rules. Official bio & portrait →
Research Scientist at Meta Reality Labs working on hand pose estimation from surface EMG. First author of the emg2pose benchmark. PhD in machine learning from the Oxford Robotics Institute, with a research internship at DeepMind.
Originators and hosts of the challenge, providing its coordination, submission platform, data standards, and reproducible evaluation.
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 →
Chief Scientific Officer at Yneuro and Honorary Research Officer at Imperial College London. PhD work on EMG biomechanics modelling. Coordinates the challenge’s day-to-day execution alongside the lead organizer and contributes to Braindecode and Codabench. Personal site →
External PhD candidate at the Donders Institute, Radboud University. Deep learning for EEG decoding with a focus on transfer learning, self-supervised learning, and foundation models. Core developer of Braindecode and MOABB.
Research scientist at Inria, MIND Team. Works on statistical ML, optimization, and signal processing for M/EEG decoding. Maintainer of benchopt and contributor to braindecode, MNE-Python, and MOABB.
Machine-learning infrastructure engineer at Yneuro. Cloud deployment, software engineering, and computational-neuroscience research projects. Owns the AWS submission and ranking pipeline.
Founder and CEO of Yneuro. CentraleSupélec and ESPCI Paris-PSL alumnus with a Stanford School of Medicine master’s degree. Supports the challenge’s neurotechnology strategy, partnerships, and long-term development.
Assistant Researcher at the Institute for Neural Computation, 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.
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.
Leads the EEGLAB project. Research Director at CNRS and Research Scientist at UC San Diego. Owns the reproducibility audit at test-freeze, which is what lets any top entry be replayed byte-for-byte from another team's pipeline.
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).
Director, Research Scientist at Google DeepMind. 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 and contributed 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.
Ask the organisers and other participants on Discord, the same community as the 2025 challenge.