All Competitors

Foundation model spanning invasive SEEG/iEEG and non-invasive EEG in one backbone, with zero- and few-shot transfer across neurological disorders.

EEG foundation model pretrained with vector-quantized self-supervision, yielding interpretable discrete codes that transfer to seizure detection.

EEG pretraining framework mapping any electrode montage to a unified topology for topology-agnostic representations that transfer across datasets.

500M-parameter transformer model pretrained on intracranial SEEG recordings for neural signal forecasting, imputation, and seizure detection.

Self-supervised foundation model for wearable photoplethysmography, trained with participant-level contrastive learning on 141,000 participants.

Self-supervised foundation model for wearable electrocardiograms, trained with participant-level contrastive learning on 141,000 participants.

Health acoustics foundation model that turns short clips of coughs and breaths into embeddings for building acoustic biomarker models with less data.

EEG foundation model that pairs a convolutional encoder with a GPT backbone, pretrained by masked-segment reconstruction for low-data BCI decoding.

Voice biomarker model that scores distress, stress, exhaustion, sleep propensity and self-esteem from a short speech recording in 11 languages.

fMRI foundation model pretrained with masked autoencoding on roughly 6,700 hours of recordings for clinical prediction and network discovery.

Vision transformer for electrocardiograms that reads the printed 12-lead ECG as an image, enabling data-efficient diagnosis from few labeled examples.

Sleep staging model that segments polysomnography from any single EEG and EOG channel pair, labelling stages at resolutions finer than 30 s epochs.

EEG foundation model pretrained on clinical recordings with a wav2vec 2.0-style contrastive task, transferring to BCI decoding and sleep staging.