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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: CrowdSource |
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license: other |
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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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|CornellWhaleChallenge|| |
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|Category|EEG| |
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|Num. Examples|12,289| |
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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|Other| |
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|Citations|[1] [2]| |
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***CrowdSourced*** consists of EEG data collected as part of a study investigating brain activity during a resting state task, which included two conditions: *eyes open* and *eyes closed*, each lasting 2 minutes. The dataset contains EEG recordings from 60 participants, but only 13 successfully completed both conditions. The recordings were captured using 14-channel EEG headsets—specifically the *Emotiv EPOC+*, *EPOC X*, and *EPOC* devices. These devices provide high-quality, wireless brainwave data that is ideal for analyzing resting-state brain activity [1]. |
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The data was initially recorded at a high frequency of 2048 Hz and later downsampled to 128 Hz for processing. To segment the data for analysis, we used a 2-second window (equivalent to 256 time steps) with a 32 time-step stride to capture the dynamics of brain activity while maintaining a manageable data size. The raw EEG data for the 13 participants, along with preprocessing steps, analysis scripts, and visualization tools, are openly available on the Open Science Framework [2]. The processed dataset consists of 12,289 multivariate time series, each of length 256 (i.e., representing 2 seconds of data per time series at a sampling rate of 128 Hz). This version of the dataset has been split into cross-validation folds based on participant. |
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[1] Nikolas S Williams, William King, Geoffrey Mackellar, Roshini Randeniya, Alicia McCormick, and Nicholas A Badcock. (2023). Crowdsourced EEG experiments: A proof of concept for remote EEG acquisition using emotivpro builder and emotivlabs. *Heliyon*, 9(8). |
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[2] Nikolas Scott Williams, William King, Roshini Randeniya, and Nicholas A Badcock. (2022). Crowdsourced. <https://osf.io/9bvgh/>. |
