All Competitors

EEG foundation model built on bidirectional Mamba blocks that scale linearly with recording length, pretrained on 21,000 hours of clinical EEG.

EEG foundation model for Alzheimer's disease detection, pretrained by contrastive learning across 13 clinical EEG datasets and 2,238 subjects.

Compact EEG foundation model whose alternating attention separates within-channel time from across-channel space, cutting attention memory sixfold.

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.

Language model over whole-night sleep stage sequences that corrects automated sleep staging and supplies features for sleep disorder diagnosis.

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

Multimodal contrastive model aligning clinical EEG with free-text reports, enabling zero-shot EEG classification from natural-language prompts.

EEG-to-language foundation model that pairs a Q-Conformer encoder with a frozen LLM to decode coherent sentences from non-invasive brain recordings.

Multi-task EEG foundation model that treats brain signals as a foreign language, pairing a text-aligned neural tokenizer with a GPT-2 backbone.

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

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.

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.