EPIC: Annotated epileptic EEG independent components for artifact reduction

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Abstract: Scalp electroencephalogram is a non-invasive multi-channel biosignal that records the brain’s electrical activity. It is highly susceptible to noise that might overshadow important data. Independent component analysis is one of the most used artifact removal methods. Independent component analysis separates data into different components, although it can not automatically reject the noisy ones. Therefore, experts are needed to decide which components must be removed before reconstructing the data. To automate this method, researchers have developed classifiers to identify noisy components. However, to build these classifiers, they need annotated data. Manually classifying independent components is a time-consuming task. Furthermore, few labelled data are publicly available. This paper presents a source of annotated electroencephalogram independent components acquired from patients with epilepsy (EPIC Dataset). This dataset contains 77,426 independent components obtained from approximately 613 hours of electroencephalogram, visually inspected by two experts, which was already successfully utilised to develop independent component classifiers

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Notes: Scientific data. - 9, 1 (2022) , 512, ISSN: 2052-4463

Event: Veröffentlichung

(where): Freiburg

(who): Universität

(when): 2022

Creator: Lopes, Fabio
Leal, Adriana
Pinto, Mauro F.
Medeiros, Julio
Dourado, Antonio
Dümpelmann, Matthias
Teixeira, César Alexandre

DOI: 10.1038/s41597-022-01524-x

URN: urn:nbn:de:bsz:25-freidok-2293527

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:35 AM CEST

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Lopes, Fabio
Leal, Adriana
Pinto, Mauro F.
Medeiros, Julio
Dourado, Antonio
Dümpelmann, Matthias
Teixeira, César Alexandre
Universität

Time of origin

2022

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Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection

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EPIC: Annotated epileptic EEG independent components for artifact reduction

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Other Objects (12)

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EPIC

Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection

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Detecting epileptic seizures with multimodal non-EEG data from wearables

EEG artifact detection using spatial harmonic analysis and support vector machines

Focused ultrasound-mediated suppression of chemically-induced acute epileptic EEG activity

EPIC

Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection

Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions

Intracerebral EEG Artifact Identification Using Convolutional Neural Networks

AnEEG: leveraging deep learning for effective artifact removal in EEG data

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EEG Artifact Removal in TMS Studies of Cortical Speech Areas

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EPIC

Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection

Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions

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