An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding.

ABSTRACT: Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG research area. Previous studies on binary EEG pathology decoding have mainly used the Temple University Hospital (TUH) Abnormal EEG Corpus (TUAB) which contains approximately 3,000 manually labelled EEG recordings. To evaluate and eventually even improve the generalisation performance of machine learning methods for EEG pathology, decoding larger, publicly available datasets is required. A number of studies addressed the automatic labelling of large open-source datasets as an approach to create new datasets for EEG pathology decoding, but little is known about the extent to which training on larger, automatically labelled dataset affects decoding performances of established deep neural networks. In this st

SUBMITTER: Kiessner AK 

PROVIDER: S-EPMC10432245 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature