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.

Brno Epilepsy Center Department of Neurology St Anne's University Hospital and Medical Faculty of Masaryk University Brno Czech Republic

CEITEC Central European Institute of Technology Masaryk University Brno Czech Republic

Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Prague Czech Republic

Department of Physiology and Biomedical Engineering Mayo Clinic Rochester USA

International Clinical Research Center St Anne's University Hospital Brno Brno Czech Republic

Mayo Systems Electrophysiology Laboratory Department of Neurology Mayo Clinic Rochester MN USA

The Czech Academy of Sciences Institute of Scientific Instruments Brno Czech Republic

doi: 10.1038/s41598-019-47854-6 PubMed

doi: 10.1007/s12021-018-9397-6 PubMed

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Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals