Leibniz Institute for Neurobiology, Magdeburg, Germany.
Forschungscampus STIMULATE, Magdeburg, Germany.
J Neural Eng. 2020 Oct 9;17(5):056012. doi: 10.1088/1741-2552/abb692.
One of the main goals of brain-computer interfaces (BCI) is to restore communication abilities in patients. BCIs often use event-related potentials (ERPs) like the P300 which signals the presence of a target in a stream of stimuli. The P300 and related approaches, however, are inherently limited, as they require many stimulus presentations to obtain a usable control signal. Many approaches depend on gaze direction to focus the target, which is also not a viable approach in many cases, because eye movements might be impaired in potential users. Here we report on a BCI that avoids both shortcomings by decoding spatial target information, independent of gaze shifts.
We present a new method to decode from the electroencephalogram (EEG) covert shifts of attention to one out of four targets simultaneously presented in the left and right visual field. The task is designed to evoke the N2pc component-a hemisphere lateralized response, elicited over the occipital scalp contralateral to the attended target. The decoding approach involves decoding of the N2pc based on data-driven estimation of spatial filters and a correlation measure.
Despite variability of decoding performance across subjects, 22 out of 24 subjects performed well above chance level. Six subjects even exceeded 80% (cross-validated: 89%) correct predictions in a four-class discrimination task. Hence, the single-trial N2pc proves to be a component that allows for reliable BCI control. An offline analysis of the EEG data with respect to their dependence on stimulation time and number of classes demonstrates that the present method is also a workable approach for two-class tasks.
Our method extends the range of strategies for gaze-independent BCI control. The proposed decoding approach has the potential to be efficient in similar applications intended to decode ERPs.
脑-机接口（BCI）的主要目标之一是恢复患者的交流能力。BCI 通常使用事件相关电位（ERP），如 P300，它表示在刺激流中存在目标。然而，P300 和相关方法本身存在局限性，因为它们需要多次刺激呈现才能获得可用的控制信号。许多方法依赖于注视方向来聚焦目标，但在许多情况下，这也是不可行的，因为潜在用户的眼球运动可能会受到损害。在这里，我们报告了一种 BCI，它通过解码空间目标信息来避免这两个缺点，而无需注视转移。
我们提出了一种新的方法，从脑电图（EEG）中解码出注意力向四个目标中的一个的隐蔽转移，这些目标同时出现在左视野和右视野中。该任务旨在诱发出 N2pc 成分——一种半球侧化反应，在对侧注视目标的枕叶头皮上引发。解码方法涉及基于数据驱动的空间滤波器估计和相关度量来解码 N2pc。
尽管跨受试者的解码性能存在差异，但 24 名受试者中有 22 名的表现明显优于随机水平。6 名受试者甚至在四分类判别任务中达到了 80%（交叉验证：89%）以上的正确预测。因此，单次 N2pc 证明是一种可用于可靠 BCI 控制的成分。对 EEG 数据进行离线分析，以了解其对刺激时间和类别数量的依赖性，表明该方法也适用于两分类任务。
我们的方法扩展了用于眼动无关 BCI 控制的策略范围。所提出的解码方法有可能在旨在解码 ERP 的类似应用中具有效率。
