论文标题
一种自适应闭环ECOG解码器，用于长期和稳定的双骨骼对外骨骼的双层控制
An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic
论文作者
论文摘要
脑部计算机界面（BCIS）仍然面临许多挑战，要从实验室中走出来，用于现实生活中的应用。一个关键在于使用慢性记录器对复杂任务的各种效应子的高性能控制。随着时间的推移，这种控制必须具有稳健的功能，并且在不连续重新校准解码器的情况下必须具有高度解码性能。在本文中，证明了四脑植物患者使用慢性植入硬膜外皮质摄影（EPICOG）植入物对外骨骼的异步控制。为此，开发了一种基于自适应的在线张量解码器：递归指数加权的马尔可夫开关多线性模型（REW-MSLM）。我们在6个月的时间内证明了使用REW-MSLM对外骨骼的8维替代双层控制及其虚拟头像的稳定性，而无需重新校准解码器。
Brain-computer interfaces (BCIs) still face many challenges to step out of laboratories to be used in real-life applications. A key one persists in the high performance control of diverse effectors for complex tasks, using chronic and safe recorders. This control must be robust over time and of high decoding performance without continuous recalibration of the decoders. In the article, asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) implant is demonstrated. For this purpose, an adaptive online tensor-based decoder: the Recursive Exponentially Weighted Markov-Switching multi-Linear Model (REW-MSLM) was developed. We demonstrated over a period of 6 months the stability of the 8-dimensional alternative bimanual control of the exoskeleton and its virtual avatar using REW-MSLM without recalibration of the decoder.
