Institute of Biomedical Engineering, University of Montreal, Montreal, Canada.
School of Optometry, University of Montreal, Montreal, Canada.
J Neural Eng. 2021 Apr 6;18(5). doi: 10.1088/1741-2552/abee51.
Brain-computer interfaces (BCIs) have been used to control the gait of a virtual self-avatar with a proposed application in the field of gait rehabilitation. Some limitations of existing systems are: (a) some systems use mental imagery (MI) of movements other than gait; (b) most systems allow the user to take single steps or to walk but do not allow both; (c) most function in a single BCI mode (cue-paced or self-paced).. The objective of this study was to develop a high performance multi-modal BCI to control single steps and forward walking of an immersive virtual reality avatar.. This system used MI of these actions, in cue-paced and self-paced modes. Twenty healthy participants participated in this study, which was comprised of four sessions across four different days. They were cued to imagine a single step forward with their right or left foot, or to imagine walking forward. They were instructed to reach a target by using the MI of multiple steps (self-paced switch-control mode) or by maintaining MI of forward walking (continuous-control mode). The movement of the avatar was controlled by two calibrated regularized linear discriminate analysis classifiers that used thepower spectral density over the foot area of the motor cortex as a feature. The classifiers were retrained after every session. For a subset of the trials, positive modified feedback (MDF) was presented to half of the participants, where the avatar moved correctly regardless of the classification of the participants' MI. The performance of the BCI was computed on each day, using different control modes.. All participants were able to operate the BCI. Their average offline performance, after retraining the classifiers was 86.0 ± 6.1%, showing that the recalibration of the classifiers enhanced the offline performance of the BCI (< 0.01). The average online performance was 85.9 ± 8.4% showing that MDF enhanced BCI performance (= 0.001). The average performance was 83% at self-paced switch control and 92% at continuous control mode.. This study reports on a first BCI to use motor imagery of the lower limbs in order to control the gait of an avatar with different control modes and different control commands (single steps or forward walking). BCI performance is increased in a novel way by combining three different performance enhancement techniques, resulting in a single high performance and multi-modal BCI system. This study also showed that the improvements due to the effects of MDF lasted for more than one session.
脑-机接口 (BCI) 已被用于控制虚拟自我化身的步态，这在步态康复领域具有一定的应用价值。现有的系统存在一些局限性：(a) 一些系统使用的运动想象不是步态；(b) 大多数系统允许用户单步行走或连续行走，但不能同时满足两种情况；(c) 大多数系统仅在单一 BCI 模式下运行（提示引导或自主模式）。本研究的目的是开发一种高性能的多模态 BCI，以控制沉浸式虚拟现实化身的单步和向前行走。该系统使用提示引导和自主模式下的这些动作的运动想象。20 名健康参与者参与了这项研究，该研究由四个不同的日子的四个不同的会话组成。参与者被提示用右脚或左脚想象向前迈出一步，或者想象向前行走。他们被指示通过使用多步的运动想象（自主切换控制模式）或通过保持向前行走的运动想象（连续控制模式）来达到目标。通过使用运动皮层脚部区域的功率谱密度作为特征，对两个经过校准的正则化线性判别分析分类器进行了调整，以控制化身的运动。每次会话后都会重新训练分类器。对于一部分试验，对一半参与者呈现了阳性修正反馈 (MDF)，无论参与者的运动想象分类如何，化身都会正确移动。使用不同的控制模式在每一天计算 BCI 的性能。所有参与者都能够操作 BCI。在重新训练分类器后，他们的平均离线性能为 86.0 ± 6.1%，表明分类器的重新校准提高了 BCI 的离线性能（<0.01）。平均在线性能为 85.9 ± 8.4%，表明 MDF 提高了 BCI 的性能（=0.001）。在自主切换控制模式下的平均性能为 83%，在连续控制模式下的平均性能为 92%。本研究报告了第一个使用下肢运动想象来控制化身步态的 BCI，该 BCI 具有不同的控制模式和不同的控制命令（单步或向前行走）。通过结合三种不同的性能增强技术，以一种新颖的方式提高了 BCI 的性能，从而实现了一个高性能的多模态 BCI 系统。本研究还表明，由于 MDF 的影响，性能的提高持续了不止一个会话。
