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- Title
Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals.
- Authors
Nejedly, Petr; Kremen, Vaclav; Sladky, Vladimir; Cimbalnik, Jan; Klimes, Petr; Plesinger, Filip; Mivalt, Filip; Travnicek, Vojtech; Viscor, Ivo; Pail, Martin; Halamek, Josef; Brinkmann, Benjamin H.; Brazdil, Milan; Jurak, Pavel; Worrell, Gregory
- 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. Measurement(s) brain measurement • physiological activity • epileptic seizure AE • Artifact • Annotation Technology Type(s) electroencephalography (EEG) • intracranial electroencephalography • data transformation Factor Type(s) institution Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12326471
- Subjects
ELECTROENCEPHALOGRAPHY; DIGITAL signal processing; MACHINE learning; DATA transformations (Statistics); HUMAN beings
- Publication
Scientific Data, 2020, Vol 7, Issue 1, p1
- ISSN
2052-4463
- Publication type
Article
- DOI
10.1038/s41597-020-0532-5