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:

https://bcmi.sjtu.edu.cn/~seed/seed-vig.html

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.

The data file contains 3 variables and they are EEGsample, substate and subindex.

"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.

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:

https://github.com/cuijiancorbin/Benchmarking-EEG-based-cross-dataset-driver-drowsiness-recognition-with-deep-transfer-learning

[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 & Biology Society (EMBC). IEEE, 2023. DOI: 10.1109/EMBC40787.2023.10340982