Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals.
Sci Data
; 7(1): 179, 2020 06 16.
Article
in En
| MEDLINE
| ID: mdl-32546753
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Brain
/
Artifacts
/
Electrocorticography
Type of study:
Clinical_trials
Limits:
Humans
Country/Region as subject:
America do norte
/
Europa
Language:
En
Journal:
Sci Data
Year:
2020
Document type:
Article
Affiliation country:
United States