Abstract
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.
Original language | English |
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Title of host publication | Australian Conference on Human-Computer Interaction |
Number of pages | 8 |
Publisher | Association for Computing Machinery |
Publication date | Dec 2020 |
Pages | 564-601 |
ISBN (Print) | 9781450389754 |
DOIs | |
Publication status | Published - Dec 2020 |
Event | 32nd Australian Conference on Human-Computer Interaction - Virtual, Sidney, Australia Duration: 2 Dec 2020 → 4 Dec 2020 http://www.ozchi.org/2020/ |
Conference
Conference | 32nd Australian Conference on Human-Computer Interaction |
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Location | Virtual |
Country/Territory | Australia |
City | Sidney |
Period | 02/12/2020 → 04/12/2020 |
Internet address |
Keywords
- Temporal Impact on Cognitive Distraction Detection
- Electroencephalography
- EEG
- Cognitive State Detection
- Cognitive Distraction
- Distraction Detection for Drivers