Extracted SEED-VIG dataset for cross-dataset driver drowsiness recognition

The SEED-VIG dataset contains EEG data from subjects conducting a monotonous driving task in a virtual simulator. The eye closure (PERCLOS) information was obtained from Senso-Motoric-Instrument (SMI) eye-tracking glasses, which was used to label the data. The dataset is officially available from:<br>https://bcmi.sjtu.edu.cn/~seed/seed-vig.html<br>We have extracted a small portion of the dataset for research on cross-dataset driver drowsiness recognition. Specifically, we down-sampled the EEG signals to 128Hz and processed the data with a low-pass filter of 1Hz. EEG samples with a 3-second length were extracted prior to the PERCLOS evaluation event. We followed the procedure adopted in [1] by labeling the samples as ‘alert’ when PERCLOS is lower than 0.35 and samples as ‘drowsy’ when PERCLOS is higher than 0.7, while the samples in the middle range were discarded. We further discarded sessions with less than 50 samples of either class and balanced the class for each session by selecting the most alert or drowsiest ones. In this way, we have 4566 samples in total from 12 subjects.<br>The data file contains 3 variables and they are EEGsample, substate and subindex.<br>"EEGsample" contains 4566 EEG samples of size 17x384 from 12 subjects. Each sample is a 3s EEG data with 128Hz from 17 EEG channels."subindex" is an array of 4566x1. It contains the subject indexes from 1-11 corresponding to each EEG sample."substate" is an array of 4566x1. It contains the labels of the samples. 0 corresponds to the alert state and 1 correspond to the drowsy state.<br>Details about our work of cross-dataset driver drowsiness recognition based on this dataset can be found from [2] and the codes are available from:<br>https://github.com/cuijiancorbin/Benchmarking-EEG-based-cross-dataset-driver-drowsiness-recognition-with-deep-transfer-learning<br><br><br>[1] W.-L. Zheng, and B.-L. Lu, "A multimodal approach to estimating vigilance using EEG and forehead EOG," Journal of neural engineering, 2017. 14(2): p. 026017.[2] Cui, Jian, et al. "Benchmarking EEG-based cross-dataset driver drowsiness recognition with deep transfer learning." 2023 45th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC). IEEE, 2023. DOI: 10.1109/EMBC40787.2023.10340982

SEED-VIG数据集包含了受试者在虚拟模拟器中执行单调驾驶任务时采集的脑电图（Electroencephalogram, EEG）数据。眼部闭合百分比（Percentage of Eye Closure, PERCLOS）信息来自眼动设备厂商Senso-Motoric-Instrument（SMI）的眼动追踪眼镜，该信息被用于对数据集进行标注。该数据集的官方获取地址为：<br>https://bcmi.sjtu.edu.cn/~seed/seed-vig.html<br>我们从中提取了小部分样本用于跨数据集驾驶员困倦识别研究。具体而言，我们将脑电图信号下采样至128Hz，并使用1Hz低通滤波器对数据进行预处理。在PERCLOS评估事件之前，我们提取了时长为3秒的脑电图样本。我们遵循文献[1]中的标注规则：当PERCLOS低于0.35时，将样本标记为"清醒"；当PERCLOS高于0.7时，标记为"困倦"，而处于中间区间的样本则予以丢弃。我们进一步剔除了任意一类样本数量少于50的会话，并通过选取最清醒或最困倦的样本，对每个会话的类别分布进行平衡。最终我们得到了来自12名受试者的共计4566个样本。该数据文件包含3个变量，分别为EEGsample、substate与subindex。"EEGsample"包含了来自12名受试者的4566个脑电图样本，每个样本的维度为17×384。每个样本为来自17个脑电图通道、采样率128Hz的3秒时长脑电图数据。"subindex"为一个4566×1的数组，其中存储了对应每个脑电图样本的受试者编号（范围为1至11）。"substate"为一个4566×1的数组，其中存储了样本的标签：0对应清醒状态，1对应困倦状态。关于我们基于该数据集开展的跨数据集驾驶员困倦识别研究的详细内容可参见文献[2]，相关代码的获取地址为：<br>https://github.com/cuijiancorbin/Benchmarking-EEG-based-cross-dataset-driver-drowsiness-recognition-with-deep-transfer-learning<br><br><br>[1] 郑伟伦与陆伯麟，《基于脑电图与前额眼电图的警觉度估计多模态方法》，《神经工程学报（Journal of Neural Engineering）》，2017年，第14卷第2期，论文编号026017。[2] 崔健等，《基于深度学习迁移学习的脑电图跨数据集驾驶员困倦识别基准研究》，2023年第45届IEEE工程医学与生物学学会年会（EMBC），IEEE，2023，DOI: 10.1109/EMBC40787.2023.10340982。

搜集汇总

背景与挑战

背景概述

SEED-VIG数据集提取部分包含12名受试者的4566个EEG样本，用于跨数据集驾驶员困倦识别研究。数据经过预处理（下采样至128Hz，1Hz低通滤波），并根据PERCLOS值标记为清醒或困倦状态，适用于深度学习迁移学习研究。