Subject Transfer

Subject transfer, in the context of brain-computer interfaces (BCIs) and other biosignal analysis, aims to improve the accuracy and efficiency of models by leveraging data from multiple subjects to predict outcomes for new, unseen individuals. Current research focuses on developing robust transfer learning methods, often employing deep neural networks (including multilayer perceptrons and those incorporating attention mechanisms) and leveraging resting-state data to reduce the need for extensive subject-specific training. This work is crucial for advancing the practical applicability of BCIs and similar technologies by minimizing the data requirements for individual users, thereby increasing accessibility and reducing the cost and time associated with model calibration.