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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of 156 filtered models
Photoplethysmography foundation model whose hierarchical convolutional encoder exposes one embedding per temporal scale and runs entirely on-watch.
EEG foundation model whose learned queries map any electrode montage into a fixed latent space, scaling linearly in the number of channels.
fMRI foundation model of the human brain connectome: 1.2B parameters and brain-environment interaction tokens for behavior and disease prediction.
EEG foundation model for brain-computer interfaces, pairing masked pretraining with a mixture-of-experts transformer across electrode montages.
Self-supervised EEG foundation model that encodes each electrode independently, so one frozen encoder serves any channel count or montage.
ECG foundation model on a bidirectional xLSTM backbone, pretrained with SimDINOv2 self-supervision to transfer across 12-lead, Holter and PPG traces.
EEG foundation model pretrained by latent diffusion instead of masked reconstruction, jointly optimizing a ViT encoder with a DiT denoiser.
Self-supervised bioacoustic audio encoder that turns animal-sound recordings into transferable embeddings for species classification and detection.
Surface-EMG wristband models that decode hand gestures, handwriting, and wrist movement, generalizing across users without per-person calibration.
Multimodal foundation model for cardiac biosignals, pretrained by masked modeling on ECG, PPG, and clinical text from ~1.7 million individuals.
Apple's foundation model trained on behavioral signals from wearables, modeling 27 HealthKit metrics to improve predictions across 57 health tasks.
EEG foundation model with cross-scale spatiotemporal tokenization and sparse structured attention, evaluated on 11 decoding tasks across 16 datasets.
Multi-scale ECG-language model that aligns 12-lead ECG signals with clinical text at token, beat, and rhythm levels for zero-shot cardiac diagnosis.
Generative foundation model for intracranial EEG whose attention separates content, time and channel, so one checkpoint fits any electrode montage.
5.6M-parameter multimodal foundation model fusing fMRI time series with diffusion-MRI structural connectivity in a shared ROI embedding space.
Transformer pretrained on raw fNIRS brain signals that scores procedural skill and transfers to unseen surgical procedures via a tiny adapter.
EEG foundation model with unified spatio-temporal attention and channel-permutation equivariance across unseen electrode montages.
Physiological signal foundation model for ECG, EMG, and EEG pairing learnable multi-scale wavelet decomposition with masked transformer pretraining.
Spatiotemporal foundation model that learns representations directly from 4D functional MRI volumes for disease diagnosis and phenotype prediction.
EEG foundation model pairing a decoupled time-frequency tokenizer with a multi-scale state-space encoder for generalization under distribution shift.
Sensor-language foundation models aligning wearable biosignals with text for zero-shot activity recognition, retrieval, and sensor captioning.
Multimodal ECG model that reads raw 12-lead waveforms and ECG images in one shared space via a structure-aware discrete signal tokenizer.