Carino-Escobar Ruben I, Rodríguez-García Martín E, Carrillo-Mora Paul, Valdés-Cristerna Raquel, Cantillo-Negrete Jessica
Division of Research in Medical Engineering, Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City, Mexico.
Electrical Engineering Department, Universidad Autónoma Metropolitana Unidad Iztapalapa, Mexico City, Mexico.
Front Neurorobot. 2023 Feb 28;17:1015464. doi: 10.3389/fnbot.2023.1015464. eCollection 2023.
Brain-Computer Interfaces (BCI) can allow control of external devices using motor imagery (MI) decoded from electroencephalography (EEG). Although BCI have a wide range of applications including neurorehabilitation, the low spatial resolution of EEG, coupled to the variability of cortical activations during MI, make control of BCI based on EEG a challenging task.
An assessment of BCI control with different feedback timing strategies was performed. Two different feedback timing strategies were compared, comprised by passive hand movement provided by a robotic hand orthosis. One of the timing strategies, the continuous, involved the partial movement of the robot immediately after the recognition of each time segment in which hand MI was performed. The other feedback, the discrete, was comprised by the entire movement of the robot after the processing of the complete MI period. Eighteen healthy participants performed two sessions of BCI training and testing, one with each feedback.
Significantly higher BCI performance (65.4 ± 17.9% with the continuous and 62.1 ± 18.6% with the discrete feedback) and pronounced bilateral alpha and ipsilateral beta cortical activations were observed with the continuous feedback.
It was hypothesized that these effects, although heterogenous across participants, were caused by the enhancement of attentional and closed-loop somatosensory processes. This is important, since a continuous feedback timing could increase the number of BCI users that can control a MI-based system or enhance cortical activations associated with neuroplasticity, important for neurorehabilitation applications.
脑机接口(BCI)能够利用从脑电图(EEG)解码出的运动想象(MI)来控制外部设备。尽管BCI有着包括神经康复在内的广泛应用,但EEG的低空间分辨率,再加上运动想象期间皮层激活的变异性,使得基于EEG的BCI控制成为一项具有挑战性的任务。
对不同反馈时间策略下的BCI控制进行了评估。比较了两种不同的反馈时间策略,由机器人手部矫形器提供的被动手部运动组成。其中一种时间策略是连续反馈,即在识别出每次执行手部运动想象的时间段后,机器人立即进行部分运动。另一种反馈是离散反馈,由在整个运动想象周期处理完成后机器人的整体运动组成。18名健康参与者进行了两个阶段的BCI训练和测试,每个反馈各进行一个阶段。
连续反馈下观察到显著更高的BCI性能(连续反馈时为65.4±17.9%,离散反馈时为62.1±18.6%)以及明显的双侧阿尔法和同侧贝塔皮层激活。
据推测,尽管这些效应在参与者之间存在差异,但它们是由注意力和闭环体感过程的增强引起的。这很重要,因为连续反馈时间可能会增加能够控制基于运动想象的系统的BCI用户数量,或者增强与神经可塑性相关的皮层激活,这对神经康复应用很重要。