5 papers
Device Passport: Enabling Spatio-Temporal Pretrained Models to Generalize Across Input Layouts
Geeling Chau, Ran Liu, Juri Minxha +5
New device layouts pose a challenging modeling problem due to the lack of large datasets for each specific layout. Biosignal foundation models offer a plausible solution if they ar…
Neuroprobe: Evaluating Intracranial Brain Responses to Naturalistic Stimuli
Andrii Zahorodnii, Christopher Wang, Geeling Chau +7
High-resolution neural datasets enable foundation models for the next generation of brain-computer interfaces and neurological treatments. The community requires rigorous benchmark…
Learning Time-Scale Invariant Population-Level Neural Representations
Eshani Patel, Yisong Yue, Geeling Chau
General-purpose foundation models for neural time series can help accelerate neuroscientific discoveries and enable applications such as brain computer interfaces (BCIs). A key com…
Learning the relative composition of EEG signals using pairwise relative shift pretraining
Christopher Sandino, Sayeri Lala, Geeling Chau +6
Self-supervised learning (SSL) offers a promising approach for learning electroencephalography (EEG) representations from unlabeled data, reducing the need for expensive annotation…
Population Transformer: Learning Population-level Representations of Neural Activity
Geeling Chau, Christopher Wang, Sabera Talukder +5
We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address key challenges in scaling models with ne…