Wittevrongel Benjamin, Van Hulle Marc M
Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium.
Front Neurosci. 2017 Nov 15;11:630. doi: 10.3389/fnins.2017.00630. eCollection 2017.
Brain-Computer Interfaces (BCIs) decode brain activity with the aim to establish a direct communication channel with an external device. Albeit they have been hailed to (re-)establish communication in persons suffering from severe motor- and/or communication disabilities, only recently BCI applications have been challenging other assistive technologies. Owing to their considerably increased performance and the advent of affordable technological solutions, BCI technology is expected to trigger a paradigm shift not only in assistive technology but also in the way we will interface with technology. However, the flipside of the quest for accuracy and speed is most evident in EEG-based visual BCI where it has led to a gamut of increasingly complex classifiers, tailored to the needs of specific stimulation paradigms and use contexts. In this contribution, we argue that spatiotemporal beamforming can serve several synchronous visual BCI paradigms. We demonstrate this for three popular visual paradigms even without attempting to optimizing their electrode sets. For each selectable target, a spatiotemporal beamformer is applied to assess whether the corresponding signal-of-interest is present in the preprocessed multichannel EEG signals. The target with the highest beamformer output is then selected by the decoder (maximum selection). In addition to this simple selection rule, we also investigated whether interactions between beamformer outputs could be employed to increase accuracy by combining the outputs for all targets into a feature vector and applying three common classification algorithms. The results show that the accuracy of spatiotemporal beamforming with maximum selection is at par with that of the classification algorithms and interactions between beamformer outputs do not further improve that accuracy.
脑机接口（BCIs）对大脑活动进行解码，目的是建立与外部设备的直接通信通道。尽管它们被誉为能为患有严重运动和/或沟通障碍的人（重新）建立沟通，但直到最近，BCI应用才开始挑战其他辅助技术。由于其性能大幅提高以及经济适用技术解决方案的出现，BCI技术不仅有望在辅助技术领域引发范式转变，还将改变我们与技术交互的方式。然而，对准确性和速度的追求带来的负面影响在基于脑电图的视觉BCI中最为明显，这导致了一系列越来越复杂的分类器，这些分类器是根据特定刺激范式和使用场景量身定制的。在本论文中，我们认为时空波束形成可服务于多种同步视觉BCI范式。我们针对三种流行的视觉范式进行了演示，甚至没有尝试优化它们的电极组。对于每个可选目标，应用时空波束形成器来评估在预处理的多通道脑电图信号中是否存在相应的感兴趣信号。然后解码器选择波束形成器输出最高的目标（最大选择）。除了这个简单规则，我们还研究了是否可以通过将所有目标的输出组合成一个特征向量并应用三种常见分类算法，利用波束形成器输出之间的相互作用来提高准确性。结果表明，最大选择的时空波束形成的准确性与分类算法相当，并且波束形成器输出之间的相互作用并没有进一步提高该准确性。
