Temporal Impact on Cognitive Distraction Detection for Car Drivers using EEG
| Autor: | Mikkel Bjerregaard Kristensen, Eike Schneiders, Mikael B. Skov, Michael Kvist Svangren |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.diagnostic_test
Computer science Evaluation data Cognitive distraction 010401 analytical chemistry 05 social sciences Cognitive Distraction Cognition Electroencephalography 01 natural sciences Session (web analytics) 0104 chemical sciences Car drivers Temporal Impact on Cognitive Distraction Detection medicine 0501 psychology and cognitive sciences EEG Distraction Detection for Drivers Cognitive State Detection 050107 human factors Cognitive psychology |
| Zdroj: | Schneiders, E, Kristensen, M R B, Svangren, M K & Skov, M B 2020, Temporal Impact on Cognitive Distraction Detection for Car Drivers using EEG . in Australian Conference on Human-Computer Interaction . Association for Computing Machinery, pp. 564-601, 32nd Australian Conference on Human-Computer Interaction, Sidney, Australia, 02/12/2020 . https://doi.org/10.1145/3441000.3441013 OZCHI |
| DOI: | 10.1145/3441000.3441013 |
| Popis: | Electroencephalography (EEG) has the potential to measure a person’s cognitive state, however, we still only have limited knowledge about how well-suited EEG is for recognising cognitive distraction while driving. In this paper, we present DeCiDED, a system that uses EEG in combination with machine learning to detect cognitive distraction in car drivers. Through DeCiDED, we investigate the temporal impact, of the time between the collection of training and evaluation data, and the detection accuracy for cognitive distraction. Our results indicate, that DeCiDED can recognise cognitive distraction with high accuracy when training and evaluation data are originating from the same driving session. Further, we identify a temporal impact, resulting in reduced classification accuracy, of an increased time-span between different drives on the detection accuracy. Finally, we discuss our findings on cognitive attention recognition using EEG how to complement it to categorise different types of distractions. |
| Databáze: | OpenAIRE |
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