Christian Scharinger

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%), 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.

We investigated the interplay between inhibition and updating, two executive working memory (WM) functions. We applied a novel task paradigm consisting of flanker stimuli presented within an n‐back task and studied the interaction between inhibitory demands and load on WM updating using behavioral measures, EEG, and pupil dilation. In contrast to studies that examine the interaction between inhibitory demands and load on WM storage components, the current task paradigm allowed testing the interaction between the executive WM components updating and inhibition. We found a reduced flanker interference effect for the highest (2‐back) updating load condition compared to lower updating load conditions on most measures. We interpret these findings as indicating that inhibitory control and WM updating are closely intertwined executive functions. Increased load on updating seemed to result in an overall more activated attentional network thus enhancing inhibitory control, such that task performance is less susceptible to distracting information.

Executive working memory functions play a central role in reading comprehension. In the present research we were interested in additional load imposed on executive functions by link-selection processes during computer-based reading. For obtaining process measures, we used a methodology of concurrent electroencephalographic (EEG) and eye-tracking data recording that allowed us to compare epochs of pure text reading with epochs of hyperlink-like selection processes in an online reading situation. Furthermore, this methodology allowed us to directly compare the two physiological load-measures EEG alpha frequency band power and pupil dilation. We observed increased load on executive functions during hyperlink-like selection processes on both measures in terms of decreased alpha frequency band power and increased pupil dilation. Surprisingly however, the two measures did not correlate. Two additional experiments were conducted that excluded potential perceptual, motor, or structural confounds. In sum, EEG alpha frequency band power and pupil dilation both turned out to be sensitive measures for increased load during hyperlink-like selection processes in online text reading.