2 citations · 2 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
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…