A Cyclic Adaptation-Generalization Framework with Uncertainty-Guided Self-Paced Learning for Long-Term Brain-Machine Interfaces
2026-07-27 • Artificial Intelligence
Artificial IntelligenceHuman-Computer InteractionRobotics
AI summaryⓘ
The authors address the problem of neural drift, which causes brain-machine interfaces (BMIs) to become less accurate over time and require frequent recalibration. They propose a new method called UnSPC that combines two existing techniques, domain adaptation and domain generalization, to better handle changes in brain signals. Their approach uses a special way to select reliable data and cycles through steps that improve the system's ability to adjust to gradual shifts in neural activity. Tests on several datasets show that their method helps maintain BMI performance over the long term. This work is the first to combine these strategies in a cycling framework with pseudo-labeling for stable BMI use.
Brain-Machine InterfaceNeural DriftDomain AdaptationDomain GeneralizationPseudo-labelingSelf-paced LearningNeural DecodingRepresentation LearningPerformance DegradationLong-term Deployment
Authors
Jiyu Wei, Di Hong, Zhanjie Zhang, Dazhong Rong, Qinming He, Yueming Wang
Abstract
Brain-Machine Interfaces (BMIs), which link the brain to external devices, hold great potential in rehabilitation, human performance augmentation, and human-centered robotics. However, invasive BMIs face a critical challenge for long-term deployment due to neural drift, which degrades decoding performance over time and necessitates frequent recalibration. Existing methods designed to mitigate neural drift typically rely on either domain adaptation (DA) or domain generalization (DG) alone and often fail to capture fine-grained distribution shifts across neural subdomains, resulting in limited performance. To overcome these limitations, we propose Uncertainty-guided Self-paced Cycling (UnSPC), a robust framework that synergizes DA and DG for target domain refining under an Uncertainty-guided Self-paced Pseudo-labeling (UnSPL) mechanism. To handle subdomain neural drift across domains, UNSPL is proposed to iteratively mine reliable pseudo-labeled samples with a noise-robust ranking strategy for further fine-tuning. Leveraging these high-quality samples, we introduce a novel Cycling Adaptation and Generalization (CycAG) strategy, which integrates DA and DG within an iterative cycle to progressively mitigate both global and subdomain drift. This cyclic process enables effective alignment to evolving target distributions while preserving robust and transferable representations, thereby mitigating performance degradation under long-term neural drifts. Extensive experiments on multiple neural decoding datasets demonstrate the effectiveness and robustness of UnSPC. To our knowledge, our proposed UnSPC is the first to cyclically integrate DA and DG with pseudo-labeling, paving the way toward stable long-term BMI controls.