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Every biological foundation model, evaluated and ranked by the bio.rodeo team
EEG model family that distills a SimCLR-pretrained SE-ResNet teacher into edge-sized students for abnormality, motor-imagery and emotion decoding.
Cardiovascular risk prediction from full-night polysomnography, fusing frozen sleep-staging ECG, EEG and EOG encoders with a Coupled Mamba engine.
Unified neural foundation model jointly pretrained on intracranial EEG and intracortical spiking activity for motor and language decoding.
Sleep foundation model whose frozen encoder scores polysomnography events, predicts incident disease, and transfers to wearable EEG and actigraphy.
Conditional flow matching model that reconstructs 4D left-ventricular geometry and motion from a 12-lead ECG and basic demographic variables.
Twelve-lead ECG foundation model aligned to clinician-overread report text, adapting to new cardiac tasks from as few as ten labels.
Physiological time-series classification model bringing in-context learning to ECG, EEG, and ICU signals with temporal and channel-aware encodings.
Self-supervised spatio-temporal transformer for resting-state EEG, pretrained on healthy controls and transferred to Alzheimer's screening.
Clinical EEG foundation model reading 17 findings — seizures, spikes, slowing, burst suppression, sleep stages — from one pretrained transformer.
EEG foundation model pairing masked contextual modelling with cross-view invariance learning over 11,000 hours of routine clinical recordings.
EEG foundation model trained jointly across nine heterogeneous datasets, decoding by matching brain signals to language-model embeddings of labels.
Bioacoustic encoder for birdsong that resolves individual syllables at 5 ms, using asymmetric spectrogram patches and Voronoi masked pretraining.
Sleep staging from one behind-the-ear electrode pair, feeding automated REM-sleep-without-atonia scoring and REM sleep behaviour disorder detection.
Self-supervised stereo-EEG encoder that localizes the seizure onset zone in drug-resistant epilepsy from peri-ictal superlet spectrograms.
Cardiac foundation model with one shared Transformer encoder for ECG, PPG, and PCG, aligning modalities in latent cardiac time via a learned delay.
Self-supervised single-lead ECG encoder pretrained on ten-minute ambulatory windows, giving patient-consistent embeddings for rhythm detection.
EEG foundation model that decodes by matching neural activity to label text embeddings, with one instruction-tuned checkpoint covering seven tasks.
ECG foundation model that reads any subset of the 12 standard leads natively, encoding recordings as variable-size spatiotemporal graphs.
EEG foundation model that corrects low-frequency bias by reconstructing band-standardized time-frequency targets. State of the art on 24 of 41 tasks.
EEG foundation model coupling spatial and temporal transformer branches through a shared soft mixture-of-experts, adapted by tuning 5.1% of weights.
EEG foundation model pretrained to predict structured latent states rather than masked waveforms, reaching 52.94% frozen macro balanced accuracy.
EEG foundation model for continuous monitoring, using windowed alternating attention to hold KV-cache memory constant on recordings up to 14 hours.