Data Sets

Identifying Cognitive Assistance with Mobile Electroencephalography: A Case Study with In-Situ Projections for Manual Assembly

Contrary to popular beliefs, manual assembly at production is a mentally demanding task. With current trends of rapid prototyping and smaller production lot sizes, this will result in frequent changes of assembly instructions that have to be memorized by workers. Assistive systems can compensate this expected increase in mental workload, specifically working memory load, by providing “just-in-time” assembly instructions through in-situ projections. The implementation of such systems and their benefits to reducing mental workload have previously been justified with self-perceived ratings or think-aloud studies. However, there is no evidence by objective measures if mental workload is truly reduced by in-situ assistance. In our work, we showcase electroencephalography as a complementary evaluation tool to assess the cognitive demand placed by two different assistive systems in an assembly task, namely paper instructions and in-situ projections. We identified the individual electroencephalographic bandwidth that varied with changes in working memory load.We show, that changes in the corresponding bandwidth are found between paper instructions and in-situ projections, indicating that they reduce working memory compared to paper instructions. These findings converge with NASA-TLX questionnaire responses for subjective workload. Methodically, the current work contributes by demonstrating how design claims of cognitive workload alleviation can be validated. Moreover, it directly validates the use of engineered assistive systems for delivering context-aware information. Finally, we analyze the characteristics of electroencephalography as real-time assessment for cognitive workload to provide insights regarding the mental demand placed by assistive systems.

Download the full data set here. Please cite the appropriate paper when using the data set in a scientific publication. The data is only available for non-commercial use.

Your Eyes Tell: Leveraging Smooth Pursuit for Assessing Cognitive Workload

A common objective for context-aware computing systems is to predict how user interfaces impact user performance regarding their cognitive capabilities. Existing approaches such as questionnaires or pupil dilation measurements either only allow for subjective assessments or are susceptible to environmental influences and user physiology. We address these challenges by exploiting the fact that cognitive workload influences smooth pursuit eye movements. We compared three trajectories and two speeds under different levels of cognitive workload within a user study (N=20). We found higher deviations of gaze points during smooth pursuit eye movements for specific trajectory types at higher cognitive workload levels. Using an SVM classifier, we predict cognitive workload through smooth pursuit with an accuracy of 99.5% for distinguishing between low and high workload as well as an accuracy of 88.1% for estimating workload between three levels of difficulty. We discuss implications and present use cases of how cognition-aware systems benefit from inferring cognitive workload in real-time by smooth pursuit eye movements.

Download the full data set here. Please cite the appropriate paper when using the data set in a scientific publication. The data is only available for non-commercial use.

The Brain Matters: A 3D Real-Time Visualization to Examine Brain Source Activation leveraging Neurofeedback

As Brain-Computer Interfaces become available to the consumer market, this provides more opportunities in analyzing brain activity in response to different external stimuli. Current output modalities often generate a lot data, such as an electroencephalogram which only displays electrode measurements. We introduce a three-dimensional real-time brain data visualization based on the measured values received by a brain-computer interface. Instead of visualizing the collected voltages by electrodes, we calculate a current density distribution to estimate the origin of electrical source which is responsible for perceived values at electrodes. Understanding where the centers of activation in the brain are allows to better understand the relationship between external stimuli and brain activity. This could be relevant in the context of information presentation for doctors to analyze pathological phenomena. A pilot study was conducted using Virtual Reality as input stimulus. Results indicate visible changes in real-time regarding brain activation.

Download the full data set here. Please cite the appropriate paper when using the data set in a scientific publication. The data is only available for non-commercial use.

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