7 papers
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
Hubert Banville, Stéphane d'Ascoli, Simon Dahan +12
Deep learning and large public datasets have recently catalyzed the proliferation of AI models for processing brain recordings. However, systematically evaluating these models rema…
NeuralSet: A High-Performing Python Package for Neuro-AI
Jean-Rémi King, Corentin Bel, Linnea Evanson +25
Artificial intelligence (AI) is increasingly central to understanding how the brain processes information. However, the integration of neuroscience and modern AI is bottlenecked by…
Laya: A LeJEPA Approach to EEG via Latent Prediction over Reconstruction
Saarang Panchavati, Uddhav Panchavati, Hiroki Nariai +2
Electroencephalography (EEG) is a widely used tool for studying brain function, with applications in clinical neuroscience, diagnosis, and brain-computer interfaces (BCIs). Recent…
Pretext Matters: An Empirical Study of SSL Methods in Medical Imaging
Vedrana IveziÄ, Mara Pleasure, Ashwath Radhachandran +7
Though self-supervised learning (SSL) has demonstrated incredible ability to learn robust representations from unlabeled data, the choice of optimal SSL strategy can lead to vastly…
From Minutes to Days: Scaling Intracranial Speech Decoding with Supervised Pretraining
Linnea Evanson, Mingfang Zhang, Hubert Banville +3
Decoding speech from brain activity has typically relied on limited neural recordings collected during short and highly controlled experiments. Here, we introduce a framework to le…
Mentality: A Mamba-based Approach towards Foundation Models for EEG
Saarang Panchavati, Corey Arnold, William Speier
This work explores the potential of foundation models, specifically a Mamba-based selective state space model, for enhancing EEG analysis in neurological disorder diagnosis. EEG, c…