Multi-channel EEG recordings during a sustained-attention driving task

• Published in: Scientific data Vol. 6; no. 1; p. 19
• Main Authors: Cao, Zehong, Chuang, Chun-Hsiang, King, Jung-Kai, Lin, Chin-Teng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.04.2019; Nature Publishing Group
• Subjects: 631/1647/1453/1450; 631/378/2649/1310; Adult; Attention; Attention task; Automobile Driving - psychology; Brain; Brain - physiology; Brain-Computer Interfaces; Computer applications; Cortex; Data analysis; Data Descriptor; Data processing; Drowsiness; EEG; Electroencephalography; Humanities and Social Sciences; Humans; Implants; Information processing; multidisciplinary; Nervous system; Psychomotor Performance; Science; Science (multidisciplinary)
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-019-0027-4

We describe driver behaviour and brain dynamics acquired from a 90-minute sustained-attention task in an immersive driving simulator. The data included 62 sessions of 32-channel electroencephalography (EEG) data for 27 subjects driving on a four-lane highway who were instructed to keep the car cruising in the centre of the lane. Lane-departure events were randomly induced to cause the car to drift from the original cruising lane towards the left or right lane. A complete trial included events with deviation onset, response onset, and response offset. The next trial, in which the subject was instructed to drive back to the original cruising lane, began 5–10 seconds after finishing the previous trial. We believe that this dataset will lead to the development of novel neural processing methodology that can be used to index brain cortical dynamics and detect driving fatigue and drowsiness. This publicly available dataset will be beneficial to the neuroscience and brain-computer interface communities. Design Type(s) behavioral data analysis objective • source-based data analysis objective • stimulus or stress design Measurement Type(s) brain activity measurement Technology Type(s) electroencephalography Factor Type(s) Sample Characteristic(s) Homo sapiens • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)