Context Sensitivity of EEG-based Workload Classification under different Affective Valence

Self Cite

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.

Section: Introductionmentioning

confidence: 58%

Electroencephalography Based Analysis of Working Memory Load and Affective Valence in an N-back Task with Emotional Stimuli

Grissmann

Faller

Scharinger

et al. 2017

Self Cite

“…The current findings suggest that the oPe might provide a more reliable neural index when monitoring artificial agents, although it is possible that the oERN may be useful in different contexts ( van Schie et al, 2004 ). Indeed, the ERN has been used in real-time passive brain–computer interface (BCI) applications or for controlling a robotic arm such as the Baxter robot ( Bryk and Raudenbush, 1987 ; Chavarriaga and del Millán, 2010 ; Zander et al, 2010 , 2016 ; Zander and Kothe, 2011 ; Chavarriaga et al, 2014 ; Grissmann et al, 2017 ; Salazar-Gomez et al, 2017 ). In a study similar to our task, participants gazed at a robot that decides between targets ( Salazar-Gomez et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Learning From the Slips of Others: Neural Correlates of Trust in Automated Agents

Visser

Beatty

Estepp

et al. 2018

With the rise of increasingly complex artificial intelligence (AI), there is a need to design new methods to monitor AI in a transparent, human-aware manner. Decades of research have demonstrated that people, who are not aware of the exact performance levels of automated algorithms, often experience a mismatch in expectations. Consequently, they will often provide either too little or too much trust in an algorithm. Detecting such a mismatch in expectations, or trust calibration, remains a fundamental challenge in research investigating the use of automation. Due to the context-dependent nature of trust, universal measures of trust have not been established. Trust is a difficult construct to investigate because even the act of reflecting on how much a person trusts a certain agent can change the perception of that agent. We hypothesized that electroencephalograms (EEGs) would be able to provide such a universal index of trust without the need of self-report. In this work, EEGs were recorded for 21 participants (mean age = 22.1; 13 females) while they observed a series of algorithms perform a modified version of a flanker task. Each algorithm’s degree of credibility and reliability were manipulated. We hypothesized that neural markers of action monitoring, such as the observational error-related negativity (oERN) and observational error positivity (oPe), are potential candidates for monitoring computer algorithm performance. Our findings demonstrate that (1) it is possible to reliably elicit both the oERN and oPe while participants monitored these computer algorithms, (2) the oPe, as opposed to the oERN, significantly distinguished between high and low reliability algorithms, and (3) the oPe significantly correlated with subjective measures of trust. This work provides the first evidence for the utility of neural correlates of error monitoring for examining trust in computer algorithms.

“…This has been effectively demonstrated in both EEG (Ruchkin et al, 1991; Ryu and Myung, 2005; So et al, 2017) and fNIRS (Power et al, 2010, 2012; Herff et al, 2013b). Other studies have further investigated changes in brain activity with respect to changes in cognitive workload during task performance using EEG (Berka et al, 2007; Brouwer et al, 2012; Gerjets et al, 2014; Hogervorst et al, 2014; Mühl et al, 2014; Ewing et al, 2016; Schultze-Kraft et al, 2016; Grissmann et al, 2017a,b; Scharinger et al, 2017; Pergher et al, 2018) and fNIRS (Ayaz et al, 2012; Herff et al, 2013a; Unni et al, 2017). It has also been shown that cognitive workload models trained on one task condition can be effectively transferred to other conditions (Baldwin and Penaranda, 2012).…”

Section: Introductionmentioning

confidence: 99%

Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG

Tremmel

Herff

Satô

et al. 2019

With the recent surge of affordable, high-performance virtual reality (VR) headsets, there is unlimited potential for applications ranging from education, to training, to entertainment, to fitness and beyond. As these interfaces continue to evolve, passive user-state monitoring can play a key role in expanding the immersive VR experience, and tracking activity for user well-being. By recording physiological signals such as the electroencephalogram (EEG) during use of a VR device, the user's interactions in the virtual environment could be adapted in real-time based on the user's cognitive state. Current VR headsets provide a logical, convenient, and unobtrusive framework for mounting EEG sensors. The present study evaluates the feasibility of passively monitoring cognitive workload via EEG while performing a classical n-back task in an interactive VR environment. Data were collected from 15 participants and the spatio-spectral EEG features were analyzed with respect to task performance. The results indicate that scalp measurements of electrical activity can effectively discriminate three workload levels, even after suppression of a co-varying high-frequency activity.