Self-Supervised Consistency Enhanced Disentangled Learning for Neural Decoding Generalization in Brain-Machine Interface
2026-07-27 • Artificial Intelligence
Artificial IntelligenceHuman-Computer InteractionRobotics
AI summaryⓘ
The authors address the problem that brain-machine interfaces (BMIs), which help people control devices using their brain signals, tend to lose accuracy over time due to changes in neural activity called neural drift. They created a new method called SSCDL that improves BMI performance by teaching the system to recognize stable brain signal patterns despite these changes. Their approach splits brain signals into parts related to movement speed, direction, and velocity, helping the system learn better. Tests showed that their method works well across multiple days, making BMIs more reliable for long-term use.
Brain-Machine InterfaceNeural DriftNeural DecodingSelf-Supervised LearningDisentangled LearningVelocityDirectionSpeedCross-Day Generalization
Authors
Jiyu Wei, Di Hong, Zhanjie Zhang, Dazhong Rong, Qinming He, Yueming Wang
Abstract
Brain-Machine Interfaces (BMIs) provide a direct communication pathway between the brain and external devices, enabling humans to control assistive and robotic technologies, with potential applications in rehabilitation, human motor augmentation, and human-centered robotics. However, due to neural drift, the performance of BMIs decreases over time, posing challenges for long-term viability, particularly for invasive BMIs (iBMIs). Existing solutions suffer from two main drawbacks: (i) difficulty in learning robust neural representations, and (ii) neglecting that neural drift varies across motor parameters (e.g., velocity, direction, and speed). To overcome these limitations, we propose Self-Supervised Consistency enhanced Disentangled Learning (SSCDL), a neural decoding generalization framework built on two key innovations. We first design a backbone model named Consistency enhanced Neural Decoder (CND), using a novel teacher-student consistency constraint with simulated neural signal perturbations to learn robust representations invariant to neural drift. Then, we employ three dedicated CNDs under the Complementary-Disentangled Generalization (CDG) mechanism, which disentangles motor signals into velocity, direction, and speed with inspiration from neural preference theory. This disentangled learning enables SSCDL to capture invariant neural representations from diverse neural preference perspectives, significantly enhancing cross-day generalization. Extensive experimental results show that SSCDL delivers state-of-the-art decoding performance, exhibiting high robustness and cross-day stability. These capabilities underscore its strong potential for long-term interaction in human-centric robotic and fine-grained assistive applications.