![]() We present a methodology about game environment system based on BCI (brain computer interface) for immersion of FPS game play. ![]() 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. However, positive as well as negative affective valence resulted in decreased classification accuracies, when compared to the neutral affective context. 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. 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. 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. In a less controlled, more naturalistic environment, a larger variety of mental processes may be active and possibly interacting. State of the art brain-computer interfaces (BCIs) largely focus on detecting single, specific, often experimentally induced or manipulated aspects of the user state. We conclude that future studies should account for potential context-specifity of EEG measures. Therefore we assume that the FAA measure might not be usable if cognitive workload is induced simultaneously. ![]() Unexpectedly, we did not find any effects for EEG measures typically used for affective valence detection (Frontal Alpha Asymmetry (FAA)). 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. These findings are supported by changes in frontal theta and parietal alpha power, parameters used for measuring of working memory load in the EEG. 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. Subjective ratings showed that the experimental task was successful in inducing working memory load as well as affective valence. 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). 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. This could potentially create interference between EEG signatures used for identification of specific mental states. However, we assume that outside the lab complex multidimensional mental states are evoked. ![]() 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).
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