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Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals
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SYSNO ASEP 0534762 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals Author(s) Nejedlý, Petr (UPT-D) RID, SAI
Křemen, V. (US)
Sladký, V. (CZ)
Cimbálník, J. (CZ)
Klimeš, Petr (UPT-D) RID, ORCID, SAI
Plešinger, Filip (UPT-D) RID, ORCID, SAI
Mivalt, F. (US)
Trávníček, Vojtěch (UPT-D) ORCID, RID, SAI
Viščor, Ivo (UPT-D) RID, ORCID, SAI
Pail, M. (CZ)
Halámek, Josef (UPT-D) RID, ORCID, SAI
Brinkmann, B. (US)
Brázdil, M. (CZ)
Jurák, Pavel (UPT-D) RID, ORCID, SAI
Worrell, G. A. (US)Number of authors 15 Article number 179 Source Title Scientific Data. - : Nature Publishing Group
Roč. 7, č. 1 (2020)Number of pages 7 s. Publication form Print - P Language eng - English Country GB - United Kingdom Keywords high-frequency oscillations ; EEG Subject RIV FS - Medical Facilities ; Equipment OECD category Medical engineering R&D Projects LTAUSA18056 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) LO1212 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) Method of publishing Open access Institutional support UPT-D - RVO:68081731 UT WOS 000542737000002 EID SCOPUS 85086581620 DOI 10.1038/s41597-020-0532-5 Annotation 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. Workplace Institute of Scientific Instruments Contact Martina Šillerová, sillerova@ISIBrno.Cz, Tel.: 541 514 178 Year of Publishing 2021 Electronic address https://www.nature.com/articles/s41597-020-0532-5
Number of the records: 1