Motivation
AI for bioelectrical signals suffers from insufficient labeled data, high noise, large variability, and study-specific data collection, leading to low efficiency, poor generalizability, and difficulties in training and deployment.
Methodology
Developing a foundation model that takes into account the unique characteristics of bioelectrical signal acquisition mechanisms.
Contribution
This enables the development of a wide range of solutions.
