No abstract

Most brain-based measures of the electroencephalogram (EEG) are used in highly controlled lab environments and only focus on narrow mental states (e.g., working memory load). However, we assume that outside the lab complex multidimensional mental states are evoked. This could potentially create interference between EEG signatures used for identification of specific mental states. In this study, we aimed to investigate more realistic conditions and therefore induced a combination of working memory load and affective valence to reveal potential interferences in EEG measures. To induce changes in working memory load and affective valence, we used a paradigm which combines an N-back task (for working memory load manipulation) with a standard method to induce affect (affective pictures taken from the International Affective Picture System (IAPS) database). Subjective ratings showed that the experimental task was successful in inducing working memory load as well as affective valence. Additionally, performance measures were analyzed and it was found that behavioral performance decreased with increasing workload as well as negative valence, showing that affective valence can have an effect on cognitive processing. These findings are supported by changes in frontal theta and parietal alpha power, parameters used for measuring of working memory load in the EEG. However, these EEG measures are influenced by the negative valence condition as well and thereby show that detection of working memory load is sensitive to affective contexts. Unexpectedly, we did not find any effects for EEG measures typically used for affective valence detection (Frontal Alpha Asymmetry (FAA)). Therefore we assume that the FAA measure might not be usable if cognitive workload is induced simultaneously. We conclude that future studies should account for potential context-specifity of EEG measures.

No abstract

Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance. To test this approach, we analyzed neural signatures of potential affective states in data collected in a paradigm where the complex user state of perceived loss of control (LOC) was induced. In this article, source localization methods were used to identify brain dynamics with source located outside but affecting the signal of interest originating from the primary motor areas, pointing to interfering processes in the brain during natural human-machine interaction. In particular, we found affective correlates which were related to perceived LOC. We conclude that additional context information about the ongoing user state might help to improve the applicability of BCIs to real-world scenarios.