Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.
MEG Core, Aalto Neuroimaging, Aalto University School of Science, Espoo, Finland.
PLoS One. 2022 Feb 23;17(2):e0264354. doi: 10.1371/journal.pone.0264354. eCollection 2022.
Brain-computer interfaces (BCI) can be designed with several feedback modalities. To promote appropriate brain plasticity in therapeutic applications, the feedback should guide the user to elicit the desired brain activity and preferably be similar to the imagined action. In this study, we employed magnetoencephalography (MEG) to measure neurophysiological changes in healthy subjects performing motor imagery (MI) -based BCI training with two different feedback modalities. The MI-BCI task used in this study lasted 40-60 min and involved imagery of right- or left-hand movements. 8 subjects performed the task with visual and 14 subjects with proprioceptive feedback. We analysed power changes across the session at multiple frequencies in the range of 4-40 Hz with a generalized linear model to find those frequencies at which the power increased significantly during training. In addition, the power increase was analysed for each gradiometer, separately for alpha (8-13 Hz), beta (14-30 Hz) and gamma (30-40 Hz) bands, to find channels showing significant linear power increase over the session. These analyses were applied during three different conditions: rest, preparation, and MI. Visual feedback enhanced the amplitude of mainly high beta and gamma bands (24-40 Hz) in all conditions in occipital and left temporal channels. During proprioceptive feedback, in contrast, power increased mainly in alpha and beta bands. The alpha-band enhancement was found in multiple parietal, occipital, and temporal channels in all conditions, whereas the beta-band increase occurred during rest and preparation mainly in the parieto-occipital region and during MI in the parietal channels above hand motor regions. Our results show that BCI training with proprioceptive feedback increases the power of sensorimotor rhythms in the motor cortex, whereas visual feedback causes mainly a gamma-band increase in the visual cortex. MI-BCIs should involve proprioceptive feedback to facilitate plasticity in the motor cortex.
脑-机接口（BCI）可以设计为具有多种反馈方式。为了在治疗应用中促进适当的大脑可塑性，反馈应该引导用户产生所需的大脑活动，并且最好与想象的动作相似。在这项研究中，我们使用脑磁图（MEG）来测量健康受试者在基于运动想象（MI）的 BCI 训练中使用两种不同反馈方式时的神经生理变化。这项研究中使用的 MI-BCI 任务持续 40-60 分钟，涉及右手或左手运动的想象。8 名受试者使用视觉反馈，14 名受试者使用本体感觉反馈完成任务。我们使用广义线性模型分析了整个会话中在 4-40 Hz 范围内的多个频率的功率变化，以找到在训练过程中功率显著增加的频率。此外，还分别对每个梯度计分析了在 alpha（8-13 Hz）、beta（14-30 Hz）和 gamma（30-40 Hz）频段的功率增加情况，以找到在整个会话中显示出显著线性功率增加的通道。这些分析应用于三种不同的条件：休息、准备和 MI。视觉反馈增强了所有条件下枕部和左颞部通道中主要高 beta 和 gamma 频段（24-40 Hz）的振幅。相比之下，本体感觉反馈主要增加了 alpha 和 beta 频段的功率。在所有条件下，都可以在多个顶叶、枕部和颞部通道中发现 alpha 频段增强，而 beta 频段的增加则发生在休息和准备期间，主要发生在顶枕部区域，而在 MI 期间则发生在手部运动区域上方的顶叶通道中。我们的结果表明，带有本体感觉反馈的 BCI 训练增加了运动皮层中感觉运动节律的功率，而视觉反馈主要在视觉皮层中引起 gamma 频段的增加。MI-BCI 应包含本体感觉反馈，以促进运动皮层的可塑性。
