Brain-Guided Self-Paced Curriculum Learning for Adaptive Human-Machine Interfaces

Human-machine interfaces (HMIs) face several challenges that hinder their long-term performance and adaptability, such as severe overfitting of machine learning models due to limited calibration data on individual users, data distribution shifts owing to changes in user-state over time, and disruptions from outlier samples caused by user distractions. To address this, we propose a novel framework called brain-guided self-paced curriculum learning (BG-SPCL) that leverages user-state information to effectively constrain the learning space for the user intention decoder (curriculum learning) and dynamically adapts the learning curriculum based on the decoder state information self-paced learning (SPL). In the curriculum learning stage, we extract the level of user distraction from brain signals and determine the feasible curriculum region. In the SPL stage, sample difficulty is inferred from the decoder loss, and the sample weights are dynamically adjusted such that the decoder progressively learns more difficult samples. We evaluated the effectiveness of our approach by conducting extensive experiments on three public brain-machine interface (BMI) benchmarks, which constitute an HMI scenario where the user’s brain signals are naturally available. Our results showed superior performance of the proposed method compared to baseline in both offline and online learning settings with no labeled user data, demonstrating the potential for practical application of our framework in both BMI and HMI systems. Our code is available at: https://github.com/yeonoi3488/bg-spcl.