Driving and Speaking: Revelations by the Head-Mounted Detection Response Task

Conti, Antonia S.; Dlugosch, Carsten; Schwarz, Felix; Bengler, Klaus

doi:10.17077/drivingassessment.1513

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…Similar results can be found in Schwalm (2009). Furthermore, similar results were found in the DRT performance of the experiment (Conti et al, 2013).…”

supporting

confidence: 86%

See 1 more Smart Citation

Driver Distraction through Conversation Measured with Pupillometry

Dlugosch¹,

Conti²,

Bengler³

2013

Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : Dr

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“…Similar results can be found in Schwalm (2009). Furthermore, similar results were found in the DRT performance of the experiment (Conti et al, 2013).…”

supporting

confidence: 86%

“…Participants responded by pressing a button on their left index finger. The results for the DRT can be found in Conti, Dlugosch, Schwarz & Bengler (2013).…”

Section: Detection Response Taskmentioning

confidence: 99%

Driver Distraction through Conversation Measured with Pupillometry

Dlugosch¹,

Conti²,

Bengler³

2013

Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design : Dr

Self Cite

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“…The n-back task is known to increase cognitive load in individuals, requiring them to use their working memory to maintain and update information in their minds. The task has been used in several studies to measure cognitive workload in various real-world scenarios, including driving, aviation, and healthcare [18]. The n-back task is an objective measure of cognitive load and can be used in conjunction with other physiological measures, such as EEG, to provide a comprehensive assessment of cognitive workload.…”

Section: Introductionmentioning

confidence: 99%

MINDSCOPE: Machine-learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation

Katinni,

Bablani

2024

Preprint

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Passive brain-computer interfaces (P-BCIs) offer the ability to dynamically adapt system interactions by inferring users’ cognitive states, such as levels of mental workload, from neural data. The P-BCI devices have many applications from analyzing and designing educational materials to monitoring the mental health of professionals operating airplanes, nuclear power plants, and airline traffic dispatchers. Electroencephalography (EEG) is the most suitable P-BCI device that can easily find applications in daily life due to its cost efficiency and device setup simplicity as compared to other devices like Functional near- infrared spectroscopy (fNIRS) or Functional Magnetic Resonance Imaging (fMRI) that are either costly or are bulky equipment that cannot be used in real life scenarios. The goal of this work is to classify the cognitive load of the user using his/her EEG output using machine learning (ML) techniques, following the MINDSCOPE (Machine Learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation) methodology. The data acquisition was done by recording the EEG signal of the users performing n-back tasks of two different load intensities (rest and 2-back). This study accurately classified user cognitive load as high or low, allowing for comparison of several ML models and their performance in cognitive load discrimination. Feature subset analysis identified the combina- tion with the highest classification accuracy, shedding light on effective feature characterization for cognitive load assessment. Depending on the context of P-BCI use, the current work gives a starting point for choosing appropriate ML paradigms and feature characterizations to incorporate for a specific cognitive assessment task.

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“…In addition to studies conducted at the AgeLab, the task has been used extensively internationally as a method of inducing graded levels of cognitive workload with studies such as [1,2,4,5,6,7,13,21,23,24,26,30] being conducted utilizing the English version of the task or translations into French, German, Chinese, Japanese, Swedish, Korean, and Polish. Various translations of materials provided by international research teams can be found at http://agelab.mit.edu/study-tools.…”

Section: Introductionmentioning

confidence: 99%

The MIT AgeLab n-back

Reimer

Gulash²,

Foley³

et al. 2014

Adjunct Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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This paper briefly describes the background of the MIT AgeLab implementation of a delayed digit recall or nback task, and the capabilities of an android application developed to implement a multi-modal version. The MIT AgeLab n-back task is a well-established methodology for inducing graded levels of cognitive workload. It has been adopted for broad use as a multimodal surrogate demand and calibration task, and recently introduced as a driver and pedestrian distraction education tool.

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Driving and Speaking: Revelations by the Head-Mounted Detection Response Task

Cited by 6 publications

References 19 publications

Driver Distraction through Conversation Measured with Pupillometry

Driver Distraction through Conversation Measured with Pupillometry

MINDSCOPE: Machine-learning Inferencing of NeuroData for Seamless Cognitive Overload Prediction and Evaluation

The MIT AgeLab n-back

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