All Competitors

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

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.

EEG foundation model whose codebook tokenizer encodes Fourier phase on the unit circle, gaining six points of balanced accuracy over LaBraM.

Autoregressive transformer pretrained on 2,000 hours of intracortical spiking activity, decoding motor intent across subjects, species, and tasks.

EEG foundation model for brain-computer interface decoding, factorizing self-attention into parallel spatial and temporal branches.

EEG foundation model that pretrains a distance-weighted electrode graph ahead of its convolutional encoder to capture inter-channel relationships.

EEG foundation model that makes each electrode its own token stream, pretrained by causal next-signal prediction over 138 electrode positions.

EEG foundation model that learns transferable brain-signal representations with a vector-quantized tokenizer and masked transformer pretraining.

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

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

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