27 citations · 41 across the 8 of their papers we have counts for
10 papers
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…
Promoting cross-modal representations to improve multimodal foundation models for physiological signals
Ching Fang, Christopher Sandino, Behrooz Mahasseni +5
Many healthcare applications are inherently multimodal, involving several physiological signals. As sensors for these signals become more common, improving machine learning methods…
Generalizable autoregressive modeling of time series through functional narratives
Ran Liu, Wenrui Ma, Ellen Zippi +8
Time series data are inherently functions of time, yet current transformers often learn time series by modeling them as mere concatenations of time periods, overlooking their funct…
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement
Gaurav Patel, Christopher Sandino, Behrooz Mahasseni +4
In this paper, we propose a framework for efficient Source-Free Domain Adaptation (SFDA) in the context of time-series, focusing on enhancing both parameter efficiency and data-sam…
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals
Ran Liu, Ellen L. Zippi, Hadi Pouransari +5
Leveraging multimodal information from biosignals is vital for building a comprehensive representation of people's physical and mental states. However, multimodal biosignals often…
Heartbeat-Based Synchronization Scheme for the Human Intranet: Modeling and Analysis
Robin Benarrouch, Ali Moin, Flavien Solt +4
Sharing a common clock signal among the nodes is crucial for communication in synchronized networks. This work presents a heartbeat-based synchronization scheme for body-worn nodes…