Framework for Electroencephalography-based Evaluation of User Experience

Frey, Jérémy; Daniel, Maxime; Castet, Julien; Hachet, Martin; Lotte, Fabien

doi:10.1145/2858036.2858525

Cited by 65 publications

(43 citation statements)

References 30 publications

(59 reference statements)

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“…They elicited three different workload levels by increasing the number of objects to be tracked and classified these workload levels independently for fNIRS and EEG and report higher classification accuracies on fNIRS data compared to EEG data. Frey et al (2016) provided a framework for EEG-based evaluation of user experience by continuously estimating mental workload, attention and interaction errors in a controlled virtual environment.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

Unni

Ihme

Jipp

et al. 2017

Front. Hum. Neurosci.

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Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver’s cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous ‘n’ speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing.

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Section: Introductionmentioning

confidence: 99%

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

Unni

Ihme

Jipp

et al. 2017

Front. Hum. Neurosci.

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“…Traditionally, brain activity has been measured through techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) whose objective is to capture changes in magnetic fields at the scalp caused by changing electrical currents in brain neurons [7]. Cognitive load is one of the indications of brain activity.…”

Section: Introductionmentioning

confidence: 99%

Measuring Cognitive Load using Eye Tracking Technology in Visual Computing

Zagermann

Pfeil

Reiterer

2016

Proceedings of the Sixth Workshop on Beyond Time and Errors on Novel Evaluation Methods for Visualization

132

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In this position paper we encourage the use of eye tracking measurements to investigate users' cognitive load while interacting with a system. We start with an overview of how eye movements can be interpreted to provide insight about cognitive processes and present a descriptive model representing the relations of eye movements and cognitive load. Then, we discuss how specific characteristics of human-computer interaction (HCI) interfere with the model and impede the application of eye tracking data to measure cognitive load in visual computing. As a result, we present a refined model, embedding the characteristics of HCI into the relation of eye tracking data and cognitive load. Based on this, we argue that eye tracking should be considered as a valuable instrument to analyze cognitive processes in visual computing and suggest future research directions to tackle outstanding issues.

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“…Having seen almost a century of continuous research and development since its first application on humans in the 1920s (Berger, 1929), electroencephalography (EEG) is now widely used in, among others, clinical settings, neuroscience, cognitive science, psychophysiology, and brain-computer interfacing, while its use continues to expand in fields such as neuroergonomics (Parasuraman & Rizzo, 2007;Frey, Daniel, Castet, Hachet, & Lotte, 2016), neurogaming (Krol, Freytag, & Zander, 2017), neuromarketing (Vecchiato et al, 2011), neuroadaptive technology (Zander, Krol, Birbaumer, & Gramann, 2016) and mobile brain/body imaging (Gramann et al, 2011). As of December 2017, PubMed reported over 140 000 publications related to EEG, with over 4 000 published in each of the past five years.…”

Section: Introductionmentioning

confidence: 99%

SEREEGA: Simulating Event-Related EEG Activity

Krol

Pawlitzki²,

Lotte

et al. 2018

Preprint

Self Cite

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Electroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual EEG recordings, ensuring that it is known beforehand which effects are present in the data. As such, simulated data can be used, among other things, to assess or compare signal processing and machine learning algorithms, to model EEG variabilities, and to design source reconstruction methods. In this paper, we present SEREEGA, short for Simulating Event-Related EEG Activity. SEREEGA is a MATLAB-based open-source toolbox dedicated to the generation of simulated epochs of EEG data. It is modular and extensible, at initial release supporting five different publicly available head models and capable of simulating multiple different types of signals mimicking brain activity. This paper presents the architecture and general workflow of this toolbox, as well as a simulated data set demonstrating some of its functions.Keywords: Electroencephalography; Simulation; Methodology; Evaluation; Ground Truth Highlights:• Simulated EEG data has a known ground truth, which can be used to validate methods.• We present a general-purpose open-source toolbox to simulate EEG data.• It provides a single framework to simulate many different types of EEG recordings.• It is modular, extensible, and already includes a number of head models and signals.• It supports noise, oscillations, event-related potentials, connectivity, and more.

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Framework for Electroencephalography-based Evaluation of User Experience

Cited by 65 publications

References 30 publications

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

Assessing the Driver’s Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study

Measuring Cognitive Load using Eye Tracking Technology in Visual Computing

SEREEGA: Simulating Event-Related EEG Activity

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