Tailoring deep learning for real-time brain-computer interfaces: From offline models to calibration-free online decoding

-- Despite the growing success of deep learning (DL) in offline brain-computer interfaces (BCIs), its adoption in real-time applications remains limited due to three primary challenges. First, most DL solutions are designed for offline decoding, making the transition to online decoding unclear . Second, the use of sliding windows in online decoding substantially increases computational complexity. Third, DL models typically require large amounts of training data, which are often scarce in BCI applications. T o address these challenges and enable real-time, cross-subject decoding without subject-specific calibration, we introduce real-time adaptive pooling (RAP), a novel parameter-free method. RAP seamlessly modifies the pooling layers of existing offline DL models to meet online decoding requirements. It also reduces computational complexity during training by jointly decoding consecutive sliding windows. T o further alleviate data requirements, our method leverages source-free domain adaptation, enabling privacy-preserving adaptation across varying amounts of target data. Our results demonstrate that RAP provides a robust and efficient framework for real-time BCI applications. It preserves privacy, reduces calibration demands, and supports co-adaptive BCI systems, paving the way for broader adoption of DL in online BCIs. These findings lay a strong foundation for developing user-centered, high-performance BCIs that facilitate immediate feedback and user learning. I. INTRODUCTION A brain-computer interface (BCI) is a system that measures brain activity and converts it into functionally useful outputs, allowing it to replace, restore, enhance, supplement, or improve the brain's natural functions [1]. By facilitating direct communication between the brain and external devices, BCIs can assist individuals with disabilities, enhance cognitive and motor abilities, and improve human-computer interaction [2], [3].