Self-paced brain-computer interface control of ambulation in a virtual reality environment
Self-paced brain-computer interface control of ambulation in a virtual reality environment
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DOI：
10.1088/1741-2560/9/5/056016
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发表时间：
2012-10-01
影响因子：
4
通讯作者：
Nenadic, Zoran
中科院分区：
文献类型：
--
作者：
Wang, Po T.;King, Christine E.;Nenadic, Zoran
Objective.脊髓损伤（SCI）通常使受影响的个体无法行走。基于脑电图（EEG）的脑-机接口（BCI）控制下肢假肢可以恢复SCI后的直观和类似身体的假肢。为了测试其可行性，作者开发并测试了一种新型的基于EEG的数据驱动BCI系统，用于在虚拟现实环境（VRE）中直观和自定进度地控制化身的振幅。Approach.八个健全的主题和一个SCI进行了以下10分钟的培训课程：主题之间交替空转和步行动觉运动图像（KMI），而他们的脑电图记录和分析，以生成特定主题的解码模型。然后，受试者进行了一个以目标为导向的在线任务，重复了五个会话，其中他们利用KMI来控制化身的线性振幅，并在VRE内的指定点连续停止十次。主要结果。受试者的平均离线训练表现为77.2 +/-11.0%，范围为64.3%（p = 0.001 76）至94.5%（p = 6.26 x 10（-23）），机会表现为50%。平均在线性能为8.5 +/- 1.1（10个）成功停止和303 +/- 53秒完成时间（完美= 211秒）。在45个在线会话中，所有受试者在44个会话中的表现与随机游走的表现有显著差异（p < 0.05）。意义通过使用数据驱动的机器学习方法来解码用户的KMI，该BCI-VRE系统仅经过10分钟的培训就可以实现直观和有目的的自主控制。以最少的训练实现这种BCI控制的能力表明，未来BCI-下肢假体系统的实现可能是可行的。
Objective. Spinal cord injury (SCI) often leaves affected individuals unable to ambulate. Electroencephalogram (EEG) based brain-computer interface (BCI) controlled lower extremity prostheses may restore intuitive and able-body-like ambulation after SCI. To test its feasibility, the authors developed and tested a novel EEG-based, data-driven BCI system for intuitive and self-paced control of the ambulation of an avatar within a virtual reality environment (VRE). Approach. Eight able-bodied subjects and one with SCI underwent the following 10-min training session: subjects alternated between idling and walking kinaesthetic motor imageries (KMI) while their EEG were recorded and analysed to generate subject-specific decoding models. Subjects then performed a goal-oriented online task, repeated over five sessions, in which they utilized the KMI to control the linear ambulation of an avatar and make ten sequential stops at designated points within the VRE. Main results. The average offline training performance across subjects was 77.2 +/- 11.0%, ranging from 64.3% (p = 0.001 76) to 94.5% (p = 6.26 x 10(-23)), with chance performance being 50%. The average online performance was 8.5 +/- 1.1 (out of 10) successful stops and 303 +/- 53 s completion time (perfect = 211 s). All subjects achieved performances significantly different than those of random walk (p < 0.05) in 44 of the 45 online sessions. Significance. By using a data-driven machine learning approach to decode users' KMI, this BCI-VRE system enabled intuitive and purposeful self-paced control of ambulation after only 10 minutes training. The ability to achieve such BCI control with minimal training indicates that the implementation of future BCI-lower extremity prosthesis systems may be feasible.