All Competitors

Sleep staging from one behind-the-ear electrode pair, feeding automated REM-sleep-without-atonia scoring and REM sleep behaviour disorder detection.

EEG foundation model that decodes by matching neural activity to label text embeddings, with one instruction-tuned checkpoint covering seven tasks.

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.

EEG foundation model that denoises, reconstructs, and upsamples scalp recordings at any electrode position. A 380M-parameter diffusion autoencoder.

EEG foundation model turning a short dry-electrode session into quantitative brain-function metrics for psychiatric and neurological assessment.

EEG foundation model pretrained on raw multi-channel waveforms via continuous-time flow matching. Leads 7 of 9 clinical and BCI benchmarks.

EEG foundation model pretrained by spectrogram reconstruction that improves online directional motor-imagery brain-computer interface control.

EEG-to-text foundation model that turns raw recordings into clinically grounded natural-language narratives instead of fixed-label classifications.

Automated sleep staging for polysomnography in Parkinson's disease and isolated REM sleep behaviour disorder, with per-epoch confidence estimates.

EEG foundation model whose learned queries map any electrode montage into a fixed latent space, scaling linearly in the number of channels.

EEG foundation model for brain-computer interfaces, pairing masked pretraining with a mixture-of-experts transformer across electrode montages.

EEG foundation model with cross-scale spatiotemporal tokenization and sparse structured attention, evaluated on 11 decoding tasks across 16 datasets.

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.

EEG foundation model pairing a decoupled time-frequency tokenizer with a multi-scale state-space encoder for generalization under distribution shift.

Brain foundation model unifying EEG and MEG in a single encoder via a shared discrete tokenizer that transfers across sensor layouts and montages.

EEG foundation model that separates channel-wise from temporal attention, pretrained on 25,000 hours of recordings spanning eight task paradigms.

Multimodal physiological foundation model spanning EEG, ECG, EOG, and EMG that keeps working when arbitrary modalities are missing at inference time.

EEG foundation model for clinical diagnosis, combining a VQ-VAE spectral tokenizer with masked token prediction for seizure and pathology detection.

Brain-to-text decoder that reconstructs typed sentences from non-invasive MEG and EEG brain recordings using a CNN, transformer, and language model.