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多通道 EEG 在持续注意驾驶任务中的记录。

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

机构信息

Discipline of ICT, School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, Hobart, TAS, Australia.

Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung, Taiwan.

出版信息

Sci Data. 2019 Apr 5;6(1):19. doi: 10.1038/s41597-019-0027-4.

Abstract

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.

摘要

我们描述了在沉浸式驾驶模拟器中进行的 90 分钟持续注意力任务中获得的驾驶员行为和大脑动态。该数据包括 27 名受试者的 62 次 32 通道脑电图 (EEG) 数据,他们被指示在四车道高速公路上保持汽车在车道中央巡航。车道偏离事件是随机诱导的,导致汽车从原来的巡航车道向左或向右车道漂移。完整的试验包括偏差开始、反应开始和反应结束事件。下一次试验,即指示受试者驶回原来的巡航车道的试验,在前一次试验结束后 5-10 秒开始。我们相信,这个数据集将导致开发新的神经处理方法,可用于对大脑皮层动态进行索引,并检测驾驶疲劳和困倦。这个公开可用的数据集将有益于神经科学和脑机接口社区。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d033/6472414/bd75fa0767bd/41597_2019_27_Fig1_HTML.jpg

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