Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals

0534762 - ÚPT 2021 RIV GB eng J - Journal Article
Nejedlý, Petr - Křemen, V. - Sladký, V. - Cimbálník, J. - Klimeš, Petr - Plešinger, Filip - Mivalt, F. - Trávníček, Vojtěch - Viščor, Ivo - Pail, M. - Halámek, Josef - Brinkmann, B. - Brázdil, M. - Jurák, Pavel - Worrell, G. A.
Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals.
Scientific Data. Roč. 7, č. 1 (2020), č. článku 179. E-ISSN 2052-4463
R&D Projects: GA MŠMT(CZ) LTAUSA18056; GA MŠMT(CZ) LO1212
Institutional support: RVO:68081731
Keywords : high-frequency oscillations * EEG
OECD category: Medical engineering
Impact factor: 6.444, year: 2020
Method of publishing: Open access
https://www.nature.com/articles/s41597-020-0532-5

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.
Permanent Link: http://hdl.handle.net/11104/0312936