A Comprehensive Review of Biosignal Foundation Models

Authors: Na Lee, Konstantinos Barmpas, Alexandros Koliousis, Yannis Panagakis, Dimitrios Adamos, Nikolaos Laskaris and Stefanos Zafeiriou

Foundation models are emerging as a promising approach to biosignal analysis, offering solutions to common challenges in learning from biosignal data, such as high variability, noise, or limited annotations. This survey provides a comprehensive review of the state-of-the-art in this rapidly advancing field. We introduce commonly used biosignals (including EEG, ECG, EMG, EOG, and PPG) and discuss their defining characteristics with respect to training large models. We present a structured survey of existing work across unimodal and multimodal settings covering data processing, feature extraction, model architectures, pre-training paradigms, and evaluation methods. We identify open challenges such as benchmarking, interpretability, and data availability and highlight possible directions for future research. By consolidating our findings from the current literature and opportunities for progress, we aim to guide the development of robust and universal foundation models for biosignals.