AI 中文总结
研究针对脑瘫儿童脑机接口系统校准时间长的问题，开发深度学习框架，采用双向长短期记忆网络及七种训练策略，经实验表明累积学习和迁移学习可大幅降低校准要求并保持高解码性能，助力实用儿科BCI系统发展。
AI 中文摘要
脑机接口神经反馈（BCI-NFT）在神经运动康复方面展现出前景，但其临床应用，尤其是在儿科人群中，因每次治疗前需长时间校准而受限。本研究开发并评估了一个深度学习框架，以降低脑瘫儿童基于运动相关皮层电位（MRCP）的运动意图检测的校准要求。在四名脑瘫儿童的27次重复踝背屈任务中收集脑电图（EEG）。使用七种训练策略评估双向长短期记忆（Bi-LSTM）网络，从传统的会话内校准到累积跨受试者学习和迁移学习。跨受试者累积学习在无会话内校准的情况下达到91%的准确率，而添加迁移学习在最少会话内校准的情况下将准确率提高到93%。两种方法均显著优于传统校准策略，并实现了最高的F1分数和接受者操作特征（ROC）性能，证明了跨会话和参与者的强大泛化能力。这些发现表明，累积学习和迁移学习可以在保持高解码性能的同时大幅降低校准要求，支持基于MRCP的临床实用儿科BCI系统的开发。
英文摘要
Brain-computer interface neurofeedback (BCI-NFT) has shown promise for neuromotor rehabilitation, but its clinical adoption -- particularly in pediatric populations -- remains limited in part by the lengthy calibration required before each therapy session. This study developed and evaluated a deep-learning framework to reduce calibration requirements for movement-related cortical potential (MRCP)-based movement-intention detection in children with cerebral palsy (CP). Electroencephalography (EEG) was collected during repeated ankle dorsiflexion tasks across 27 sessions in four children with CP. A bidirectional long short-term memory (Bi-LSTM) network was evaluated using seven training strategies, ranging from conventional within-session calibration to cumulative cross-subject learning and transfer learning. Cross-subject cumulative learning achieved 91\% accuracy without within-session calibration, while the addition of transfer learning increased accuracy to 93\% with minimal within-session calibration. Both approaches significantly outperformed conventional calibration strategies and achieved the highest F1-scores and receiver operating characteristic (ROC) performance, demonstrating robust generalization across sessions and participants. These findings show that cumulative learning and transfer learning can substantially reduce calibration requirements while maintaining high decoding performance, supporting the development of clinically practical MRCP-based pediatric BCI systems
