This repository contains the dataset described in:
L. Wang-Nöth, P. Heiler, H. Huang, D. Lichtenstern, A. Reichenbach, L. Flacke, L. Maisch, H. Mayer, "How Much Data is Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings", preprint, 2024.
[Doi coming soon].

This dataset is licensed under the Creative Commons Attribution 4.0 (CC-BY 4.0). Please cite the original article mentioned above.

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Lu W 9e544202cc gin commit from HP-V		1 mesiac pred
EO	71a7936389 Dateien hochladen nach 'EO'	1 mesiac pred
artifacts	0dde11e35c Dateien hochladen nach 'artifacts'	1 mesiac pred
LICENSE	9ba18e8b9a Initial commit	1 mesiac pred
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datacite.yml	17cd1d96d8 'datacite.yml' ändern	1 mesiac pred
epochID_mapping.txt	9e544202cc gin commit from HP-V	1 mesiac pred

BREAD (brainboost EEG Artifact Detection)

The dataset BREAD (BRainboost Eeg Artifact Detection), used for EMG artifact detection in EEG recordings, contains both artifact-contaminated signals and resting-state eyes-open (EO) signals. It includes 932 numpy files of EEG recordings from seven subjects, consisting of 664 artifact-containing epochs and 268 EO epochs.

Each subject (identified by a subjectID ranging from 5 to 11; note that subjects 1 to 4 are not included in this dataset) participated in seven isometric contraction artifact tasks, each lasting 5 seconds and repeated 10 times, as well as five continuous movement tasks, each lasting 10 seconds and repeated 5 times. This results in 95 artifact-containing epochs per subject, with the exception of subject 7, who had one less repetition for the "kh_a" artifact task.

Additionally, each subject provided EO recordings as well, which were segmented into alternating 10-second and 5-second epochs without overlap. On average, each subject contributed 38 ± 7 EO epochs.

Epochs were extracted from the original EDF files for each subject. All subjects, except subject 5, had one EDF file containing all the necessary epochs. For subject 5, the epochs were spread across two EDF files. Each numpy file represents a single epoch.

Numpy File Naming Schema:
[subjectID]_[edfID]_eeg_[epochID]_[epochNumber]
[subjectID]: Ranges from 5 to 11.
[edfID]: 1 or 2 for subject 5, 1 for all other subjects.
[epochID]: see epochID_mapping.txt
[epochNumber]: Ranges from 0 to 4 for continuous movements and from 0 to 9 for isometric contractions. For EO, it is formatted as [EO recording Number]-[segment number].

File Name Examples:
5_1_eeg_EO_0-2.npy: This file contains the 2nd epoch of EO recording 0, segmented from the 1st EDF file of subject 5.
5_2_eeg_kb_db_0.npy: This file contains the 0th epoch of the "kb_db" artifact from the 2nd EDF file of subject 5.

Additional Information for Reading the Data:
Data shape in numpy file: channel * time
Sampling rate: 2048Hz
Unit: Volts
Channel names: see ch_name.txt

For further details or citation, refer to the accompanying publication [Coming Soon].

README.md

BREAD (brainboost EEG Artifact Detection)