优化脑卒中脑机接口康复方案：任务设计与训练时长的探索
Optimizing BCI Rehabilitation Protocols for Stroke: Exploring Task Design and Training Duration
AI总结：
本研究提出用患侧手运动想象与静息对比替代传统左右手想象，以简化任务并改善脑卒中患者对侧激活弱的问题，实验表明该方法在FBCSP和EEGNet上性能更优，且较短训练时段效果更好。
摘要：
脑卒中是导致长期残疾的主要原因，也是全球第二大常见死因。尽管急性期治疗已取得进展，但康复仍然充满挑战且效果有限。脑机接口（BCI）通过促进神经可塑性，已成为脑卒中后康复的一种有前景的工具。然而，临床结果仍存在差异，最佳方案尚未确立。本研究通过比较患侧手部运动想象与静息状态（而非传统的左右手运动想象）来探索优化基于BCI的康复策略。这种替代方案旨在简化任务，并解决脑卒中患者常见的对侧激活较弱的问题。研究使用了两个数据集，一个来自健康个体，另一个来自脑卒中患者，以评估所提出的方法。结果显示，使用FBCSP和EEGNet均获得了改进的性能。此外，我们研究了训练时长的影响，发现较短的训练时段比较长的时段能产生更好的BCI性能。
英文摘要：
Stroke is a leading cause of long-term disability and the second most common cause of death worldwide. Although acute treatments have advanced, recovery remains challenging and limited. Brain-computer interfaces (BCIs) have emerged as a promising tool for post-stroke rehabilitation by promoting neuroplasticity. However, clinical outcomes remain variable, and optimal protocols have yet to be established. This study explores strategies to optimize BCI-based rehabilitation by comparing motor imagery of affected hand movement versus rest, instead of the conventional left-versus-right motor imagery. This alternative aims to simplify the task and address the weak contralateral activation commonly observed in stroke patients. Two datasets, one from healthy individuals and one from stroke patients, were used to evaluate the proposed approach. The results showed improved performance using both FBCSP and EEGNet. Additionally, we investigated the impact of session duration and found that shorter training sessions produced better BCI performance than longer sessions.
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