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--- |
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tags: |
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- time series |
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- time series classification |
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- monster |
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- EEG |
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pretty_name: STEW |
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license: cc-by-4.0 |
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size_categories: |
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- 10K<n<100K |
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--- |
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Part of MONSTER: <https://arxiv.org/abs/2502.15122>. |
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|STEW|| |
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|Category|EEG| |
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|Num. Examples|28,512| |
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|Num. Channels|14| |
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|Length|256| |
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|Sampling Freq.|128 Hz| |
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|Num. Classes|2| |
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|License|[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)| |
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|Citations|[1] [2]| |
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***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]. The processed dataset consists of 28,512 multivariate time series each of length 256 (i.e., representing 2 seconds of data at 128 Hz). |
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[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. |
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[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. |
