İşcan Zafer, Nikulin Vadim V
Centre for Cognition and Decision Making, National Research University Higher School of Economics, Moscow, Russian Federation.
Cognitive Neuroimaging Unit, CEA DRF/Joliot Institute, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.
PLoS One. 2018 Jan 23;13(1):e0191673. doi: 10.1371/journal.pone.0191673. eCollection 2018.
Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The results can be used to improve individual classification performance taking into account effects of perturbations.
脑机接口（BCI）范式通常是在刻意避免环境和生物伪迹的情况下进行测试的。在本研究中，我们故意引入不同的干扰因素，以测试基于稳态视觉诱发电位（SSVEP）的BCI的鲁棒性。具体而言，我们研究了性能下降在多大程度上与脑电图信号质量下降有关，还是由于认知负荷增加所致。在在线任务中，受试者专注于四个圆圈中的一个，并在四种随机分配给受试者的条件下对分类的正确性给出反馈：对照（无干扰）、说话（大声反复从一到十计数）、思考（在心里反复从一到十计数）和倾听（听从一到十的口头计数）。使用决策树、朴素贝叶斯和K近邻分类器，利用典型相关分析生成的特征来评估分类性能。在在线状态下，与对照条件相比，说话和思考适度降低了平均分类准确率，而在所有受试者中，倾听和对照条件之间没有显著差异。这些性能对分类方法和干扰条件敏感。除了在θ频段外，我们在感兴趣的频率范围内的干扰期间未在脑电图中观察到明显的伪迹。因此，我们得出结论，性能下降可能源于认知因素。在倾听条件下，包括主要位于左半球的中央和颞区在内的广泛区域的相对α功率与性能呈负相关，因此很可能表明对回放的干扰性呈现进行了主动抑制。这是第一项系统评估自然伪迹（即心理、言语和音频干扰）对基于SSVEP的BCI影响的研究。考虑到干扰的影响，这些结果可用于提高个体分类性能。
