Publication result detail

Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals

NEJEDLÝ, P. KREMEN V.; SLADKÝ, V.; CIMBÁLNÍK J.; KLIMEŠ P.;PLEŠINGER, F.; MÍVALT, F.; TRÁVNÍČEK, V.; VISCOR I.; PAIL, M.; HALÁMEK, J.; BRINKMANN, B.;BRAZDIL M.; PAVEL J.; WORRELL, G.

Original Title

Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals

English Title

Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals

Type

WoS Article

Original Abstract

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.

English abstract

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.

Keywords

HIGH-FREQUENCY OSCILLATIONS

Key words in English

HIGH-FREQUENCY OSCILLATIONS

Authors

NEJEDLÝ, P. KREMEN V.; SLADKÝ, V.; CIMBÁLNÍK J.; KLIMEŠ P.;PLEŠINGER, F.; MÍVALT, F.; TRÁVNÍČEK, V.; VISCOR I.; PAIL, M.; HALÁMEK, J.; BRINKMANN, B.;BRAZDIL M.; PAVEL J.; WORRELL, G.

RIV year

2021

Released

26.06.2020

Publisher

NATURE PUBLISHING GROUP

Location

LONDON

ISBN

2052-4463

Periodical

Scientific Data

Number

7

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

7

Pages count

7

URL

https://www.nature.com/articles/s41597-020-0532-5

BibTex

@article{BUT165222,
  author="NEJEDLÝ, P. KREMEN V. and SLADKÝ, V. and CIMBÁLNÍK J. and KLIMEŠ P. and PLEŠINGER, F. and MÍVALT, F. and TRÁVNÍČEK, V. and VISCOR I. and PAIL, M. and HALÁMEK, J. and BRINKMANN, B. and BRAZDIL M. and PAVEL J. and WORRELL, G.",
  title="Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals",
  journal="Scientific Data",
  year="2020",
  number="7",
  pages="1--7",
  doi="10.1038/s41597-020-0532-5",
  url="https://www.nature.com/articles/s41597-020-0532-5"
}