The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets
library). If this is not possible, please
open a discussion
for direct help.
Part of MONSTER: https://arxiv.org/abs/2502.15122.
STEW comprises raw EEG recordings from 48 participants involved in a multitasking workload experiment [1]. Additionally, the subjects' baseline brain activity at rest was recorded before the test. The data was captured using the Emotiv Epoc device with a sampling frequency of 128Hz and 14 channels, resulting in 2.5 minutes of EEG recording for each case. Participants were instructed to assess their perceived mental workload after each stage using a rating scale ranging from 1 to 9, and these ratings are available in a separate file. The dataset has been divided into cross-validation folds based on individual participants. Additionally, binary class labels have been assigned to the data, categorizing workload ratings above 4 as "high" and those below or equal to 4 as "low". We utilize these labels for our specific problem. STEW can be accessed upon request through the IEEE DataPort [2].
[1] Wei Lun Lim, Olga Sourina, and Lipo Wang. (2018). STEW: Simultaneous task EEG workload data set. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(11):2106–2114.
[2] Wei Lun Lim, Olga Sourina, and Lipo Wang. (2020). STEW: Simultaneous task EEG workload data set. https://ieee-dataport.org/open-access/stew-simultaneous-task-eeg-workload-dataset. CC BY 4.0.
- Downloads last month
- 66