EEG artifacts are defined as waveforms that are not of cerebral origin and may be affected by numerous external and or physiological factors. These extraneous signals are often mistaken for seizures due to their morphological similarity in amplitude and frequency [4]. Artifacts often lead to raised false alarm rates in machine learning systems, which poses a major challenge for machine learning research. Most state-of-the-art systems use some forms of artifact reduction technology to suppress these events [5].
Abstract:
The Neural Engineering Data Consortium has recently developed a new subset of its popular open source electroencephalogram (EEG) corpus - TUH EEG (TUEG) [1]. The TUEG Cor...Show MoreMetadata
Abstract:
The Neural Engineering Data Consortium has recently developed a new subset of its popular open source electroencephalogram (EEG) corpus - TUH EEG (TUEG) [1]. The TUEG Corpus is the world's largest open source corpus of EEG data and currently has over 3,300 subscribers. There are several valuable subsets of this data, including the TUH Seizure Detection Corpus (TUSZ) [2], which was featured in the Neureka 2020 Epilepsy Challenge [3]. In this poster, we present a new subset of the TUEG Corpus - the TU Artifact Corpus. This corpus contains 310 EEG files in which every artifact has been annotated. This data can be used to evaluate artifact reduction technology. Since TUEG is comprised of actual clinical data, the set of artifacts appearing in the data is rich and challenging.
Date of Conference: 05-05 December 2020
Date Added to IEEE Xplore: 17 February 2021
ISBN Information: