Vidaurre Carmen, Haufe Stefan, Jorajuría Tania, Müller Klaus-Robert, Nikulin Vadim V
Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain.
Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Front Neurosci. 2020 Dec 18;14:575081. doi: 10.3389/fnins.2020.575081. eCollection 2020.
Brain-Computer Interfaces (BCIs) are systems that allow users to control devices using brain activity alone. However, the ability of participants to command BCIs varies from subject to subject. About 20% of potential users of sensorimotor BCIs do not gain reliable control of the system. The inefficiency to decode user's intentions requires the identification of neurophysiological factors determining "good" and "poor" BCI performers. One of the important neurophysiological aspects in BCI research is that the neuronal oscillations, used to control these systems, show a rich repertoire of spatial sensorimotor interactions. Considering this, we hypothesized that neuronal connectivity in sensorimotor areas would define BCI performance. Analyses for this study were performed on a large dataset of 80 inexperienced participants. They took part in a calibration and an online feedback session recorded on the same day. Undirected functional connectivity was computed over sensorimotor areas by means of the imaginary part of coherency. The results show that post- as well as pre-stimulus connectivity in the calibration recording is significantly correlated to online feedback performance in μ and feedback frequency bands. Importantly, the significance of the correlation between connectivity and BCI feedback accuracy was not due to the signal-to-noise ratio of the oscillations in the corresponding post and pre-stimulus intervals. Thus, this study demonstrates that BCI performance is not only dependent on the amplitude of sensorimotor oscillations as shown previously, but that it also relates to sensorimotor connectivity measured during the preceding training session. The presence of such connectivity between motor and somatosensory systems is likely to facilitate motor imagery, which in turn is associated with the generation of a more pronounced modulation of sensorimotor oscillations (manifested in ERD/ERS) required for the adequate BCI performance. We also discuss strategies for the up-regulation of such connectivity in order to enhance BCI performance.
脑机接口（BCIs）是一种允许用户仅通过大脑活动来控制设备的系统。然而，参与者对脑机接口的控制能力因人而异。约20%的感觉运动脑机接口潜在用户无法获得对系统的可靠控制。解码用户意图的低效性需要识别决定脑机接口表现“好”与“差”的神经生理因素。脑机接口研究中一个重要的神经生理方面是，用于控制这些系统的神经元振荡表现出丰富的空间感觉运动相互作用。考虑到这一点，我们假设感觉运动区域的神经元连接性将决定脑机接口的性能。本研究的分析是在一个由80名无经验参与者组成的大型数据集上进行的。他们参加了同一天记录的校准和在线反馈环节。通过相干性的虚部计算感觉运动区域的无向功能连接性。结果表明，校准记录中刺激后以及刺激前的连接性与μ频段和反馈频段的在线反馈性能显著相关。重要的是，连接性与脑机接口反馈准确性之间相关性的显著性并非由于相应刺激后和刺激前间隔中振荡的信噪比。因此，本研究表明，脑机接口性能不仅如先前所示依赖于感觉运动振荡的幅度，还与先前训练环节中测量的感觉运动连接性有关。运动和体感系统之间这种连接性的存在可能有助于运动想象，而运动想象又与产生足够的脑机接口性能所需的更明显的感觉运动振荡调制（表现为事件相关去同步化/事件相关同步化）相关。我们还讨论了上调这种连接性以提高脑机接口性能的策略。
