EEG-based brain-computer interface for real-time communication of patients in completely locked-in state

Changhee Han, C. Im
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引用次数: 10

Abstract

In this study, we developed a practical EEG-based BCI paradigm for online binary communication of patients in completely locked-in state (CLIS). The performance of our BCI paradigm was evaluated with a female patient in CLIS, who had never communicated even with her family for more than a year. An average online classification accuracy of 87.5 % was achieved using EEG data recorded just for 5 seconds. This is the first report of successful application of EEG-based BCI to the online yes/no communication of patients in CLIS.
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基于脑电图的脑机接口,实现患者完全闭锁状态下的实时交流
在这项研究中,我们开发了一种实用的基于脑电图的脑机接口模式,用于完全锁定状态(CLIS)患者的在线二进制通信。我们的脑机接口模式的性能评估与女性患者的CLIS,谁从未与她的家人沟通超过一年。使用记录5秒的脑电图数据,平均在线分类准确率达到87.5%。这是首个基于脑电图的脑机接口成功应用于CLIS患者在线是/否沟通的报道。
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