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@article{1670396,
author = {Nejedly, P. and Kremen, V. and Sladky, V. and Cimbalnik, J. and Klimes, P. and Plesinger, F. and Mivalt, F. and Travnicek, V. and Viscor, I. and Pail, Martin and Halamek, J. and Brinkmann, B. H. and Brázdil, Milan and Jurak, P. and Worrell, G.},
article_location = {LONDON},
article_number = {1},
doi = {http://dx.doi.org/10.1038/s41597-020-0532-5},
keywords = {HIGH-FREQUENCY OSCILLATIONS},
language = {eng},
issn = {2052-4463},
journal = {Scientific Data},
title = {Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals},
url = {https://www.nature.com/articles/s41597-020-0532-5.pdf},
volume = {7},
year = {2020}
}
TY - JOUR
ID - 1670396
AU - Nejedly, P. - Kremen, V. - Sladky, V. - Cimbalnik, J. - Klimes, P. - Plesinger, F. - Mivalt, F. - Travnicek, V. - Viscor, I. - Pail, Martin - Halamek, J. - Brinkmann, B. H. - Brázdil, Milan - Jurak, P. - Worrell, G.
PY - 2020
TI - Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals
JF - Scientific Data
VL - 7
IS - 1
SP - 1-7
EP - 1-7
PB - NATURE PUBLISHING GROUP
SN - 20524463
KW - HIGH-FREQUENCY OSCILLATIONS
UR - https://www.nature.com/articles/s41597-020-0532-5.pdf
L2 - https://www.nature.com/articles/s41597-020-0532-5.pdf
N2 - EEG signal processing is a fundamental method for neurophysiology research and clinical neurology practice. Historically the classification of EEG into physiological, pathological, or artifacts has been performed by expert visual review of the recordings. However, the size of EEG data recordings is rapidly increasing with a trend for higher channel counts, greater sampling frequency, and longer recording duration and complete reliance on visual data review is not sustainable. In this study, we publicly share annotated intracranial EEG data clips from two institutions: Mayo Clinic, MN, USA and St. Anne's University Hospital Brno, Czech Republic. The dataset contains intracranial EEG that are labeled into three groups: physiological activity, pathological/epileptic activity, and artifactual signals. The dataset published here should support and facilitate training of generalized machine learning and digital signal processing methods for intracranial EEG and promote research reproducibility. Along with the data, we also propose a statistical method that is recommended for comparison of candidate classifier performance utilizing out-of-institution/out-of-patient testing.
ER -
NEJEDLY, P., V. KREMEN, V. SLADKY, J. CIMBALNIK, P. KLIMES, F. PLESINGER, F. MIVALT, V. TRAVNICEK, I. VISCOR, Martin PAIL, J. HALAMEK, B. H. BRINKMANN, Milan BRÁZDIL, P. JURAK and G. WORRELL. Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals. \textit{Scientific Data}. LONDON: NATURE PUBLISHING GROUP, 2020, vol.~7, No~1, p.~1-7. ISSN~2052-4463. Available from: https://dx.doi.org/10.1038/s41597-020-0532-5.
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