State of the art brain-computer interfaces (BCIs) largely focus on detecting single, specific, often experimentally induced or manipulated aspects of the user state. In a less controlled, more naturalistic environment, a larger variety of mental processes may be active and possibly interacting. When moving BCI applications from the lab to real-life applications, these additional unaccounted mental processes could interfere with user state decoding, thus decreasing system efficacy and decreasing real-world applicability. Here, we assess the impact of affective valence on classification of working memory load, by re-analyzing a dataset that used an affective N-back task with picture stimuli. Our analyses showed that classification of working memory load under affective valence can lead to good classification accuracies (> 70 percent), which can be further improved via data integration over time. However, positive as well as negative affective valence resulted in decreased classification accuracies, when compared to the neutral affective context. Furthermore, classifiers failed to generalize across affective contexts, highlighting the need for user state models that can account for different contexts or new, context-independent, EEG features.

中文翻译：

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不同情感效价下基于脑电图的工作负荷分类的上下文敏感性

最先进的脑机接口 (BCI) 主要集中在检测用户状态的单个、特定、通常通过实验诱导或操纵的方面。在控制较少、更自然的环境中，更多种类的心理过程可能处于活跃状态并可能相互作用。当将 BCI 应用程序从实验室转移到现实生活应用程序时，这些额外的未考虑的心理过程可能会干扰用户状态解码，从而降低系统效率并降低现实世界的适用性。在这里，我们通过重新分析使用带有图片刺激的情感 N-back 任务的数据集来评估情感效价对工作记忆负荷分类的影响。我们的分析表明，在情感效价下对工作记忆负荷进行分类可以产生良好的分类准确率（> 70%），随着时间的推移，可以通过数据集成进一步改进。然而，与中性情感背景相比，积极和消极的情感效价会导致分类准确性降低。此外，分类器未能在情感上下文中进行泛化，突出了对可以解释不同上下文或新的、独立于上下文的 EEG 特征的用户状态模型的需求。