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

By Zehong Cao, Chun-Hsiang Chuang, Jung-Kai King, Chin-Teng Lin. Scientific Data, 2018

296 citations recorded by the source corpus.

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 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)

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/f772c5599656a92791c886f70278fa5eaa556725

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Chin-Teng Lin
2. Peng Xu
3. Alireza Jolfaei
4. Xiaotong Gu
5. Zehong Cao
6. Tzyy-Ping Jung
7. Dongrui Wu
8. T. Mcginnity

In Experimental and Cognitive Psychology

1. Amir Hussain
2. Mufti Mahmud
3. Dongrui Wu
4. Peng Xu
5. M. S. Kaiser
6. Erik Cambria
7. Soujanya Poria

In Social Psychology

1. Amir Hussain

Citations in the corpus

• EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, background): “Driving fatigue detection research has been attracting considerable attention in the BCI community [95], [96], especially in”
• Deep Learning in Mining Biological Data (2020, methodology): “Starting with the EEG signals, the SAD mc-EEG [193] dataset contains 32 channel EEG signals from 27 subjects recorded while they were test-driving.”