Analysis of Relationship between Electroencephalograms and Subjective Measurements for In-Vehicle Information System: A Preliminary Study

Kim, Nahyeong; Choe, Mungyeong; Park, Jaehyun; Park, Jung Chul; Kim, Hyun Koo; Kim, Jungyoon; Hussain, Muhammad; Jung, Suhwan

doi:10.3390/ijerph182212173

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…They reported some inconsistency between EEG-based indices and self-rating results and heterogeneity among individuals. While the valid relationship between Emotiv's performance metrics and questionnaire results is reported in another study (Kim et al, 2021), the use of these indices seems more suited for the investigation of affective states, rather than the measurement of cognitive load.…”

Section: Eegmentioning

confidence: 91%

Measuring cognitive load in augmented reality with physiological methods: A systematic review

Suzuki,

Wild,

Scanlon

2023

Computer Assisted Learning

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BackgroundCognitive load during AR use has been measured conventionally by performance tests and subjective rating. With the growing interest in physiological measurement using non‐invasive biometric sensors, unbiased real‐time detection of cognitive load in AR is expected. However, a range of sensors and parameters are used in various subject fields, and reported results are fragmented.ObjectivesThe aim of this review is to analyse systematically how physiological methods have been used to measure cognitive load and what the implications are for the future research on AR‐based tools.MethodsThis paper took the systematic review approach. Through screening with 10 exclusion criteria, 23 studies, that contain 3 key elements: AR‐based intervention, cognitive state examination and physiological methods, were identified, analysed and synthesised.ResultsPhysiological methods in their current form require reference to provide meaningful interpretations and suggestions. Therefore, they are often combined with conventional methods. Many studies investigate the effect of wearable devices in comparison with non‐AR stimuli, which has been controversial, but detection of different causes of cognitive load are on the horizon. Eye‐tracking is the method most used and most consistent in the use of its parameters.ConclusionsA multi‐method approach combining two or more evaluation instruments is essential for the validation of users' cognitive state. In addition to the AR stimuli in question, having another independent variable such as task difficulty in experiment design is useful. Statistical approaches with more data input could help establish a reliable scale. The future research should attempt to dissociate cognitive load caused by different effects such as device, instruction, and other AR techniques as well as intrinsic and extraneous aspects, in a better experimental setup with multiple parameters.

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Section: Eegmentioning

confidence: 91%

Measuring cognitive load in augmented reality with physiological methods: A systematic review

Suzuki,

Wild,

Scanlon

2023

Computer Assisted Learning

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“…And "posture" involving head, face, hand and body features is mainly recorded by camera [84]. Moreover, "subjective measure" of distracted drivers could be analyzed alone or together with the aforementioned objective data [85].…”

Section: Plos Onementioning

confidence: 99%

Understanding the domain of driving distraction with knowledge graphs

Feng

2022

PLoS ONE

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This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including “objective factors”, “human factors”, “research methods”, “data” and “data science”. The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.

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