High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton.
High accuracy decoding of user intentions using EEG to control a lower-body exoskeleton.
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DOI：
10.1109/embc.2013.6610821
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Contreras-Vidal JL
中科院分区：
文献类型：
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作者：
Kilicarslan A;Prasad S;Grossman RG;Contreras-Vidal JL
脑机接口（BMI）系统允许用户使用他们的思想控制外部机械系统。在文献中常用的是侵入性技术，以获取大脑信号和解码用户的尝试运动，以驱动这些系统（例如，机器人操纵器）。在这项工作中，我们使用下半身外骨骼，并使用非侵入性脑电图（EEG）测量用户的大脑活动。本研究的主要目的是解码截瘫患者的运动意图，并为他提供相应的下半身外骨骼行走能力。我们提出了一种新的解码方法，具有很高的离线评估精度（约98%），我们的闭环实现结构具有相当短的现场训练时间（约38秒），以及来自截瘫测试对象的实时闭环实现（NeuroRex）的初步结果。
Brain-Machine Interface (BMI) systems allow users to control external mechanical systems using their thoughts. Commonly used in literature are invasive techniques to acquire brain signals and decode user’s attempted motions to drive these systems (e.g. a robotic manipulator). In this work we use a lower-body exoskeleton and measure the users brain activity using non-invasive electroencephalography (EEG). The main focus of this study is to decode a paraplegic subject’s motion intentions and provide him with the ability of walking with a lower-body exoskeleton accordingly. We present our novel method of decoding with high offline evaluation accuracies (around 98%), our closed loop implementation structure with considerably short on-site training time (around 38 sec), and preliminary results from the real-time closed loop implementation (NeuroRex) with a paraplegic test subject.