Context Sensitivity of EEG-Based Workload Classification Under Different Affective Valence

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.