EPIC: Annotated epileptic EEG independent components for artifact reduction

Lopes, Fábio; Leal, Adriana; Medeiros, Julio; Pinto, Mauro F; Dourado, António; Dümpelmann, Matthias; Teixeira, César

doi:10.1038/s41597-022-01524-x

Cited by 3 publications

(1 citation statement)

References 38 publications

(23 reference statements)

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“…Available link. [41] This database is composed of a source of annotated electroencephalogram-independent components acquired from epileptic patients. This dataset is composed of 77,246 components obtained from 613 hours of electroencephalogram, which is a non-invasive multichannel bio-signal that records the brain's electrical activity.…”

Section: Epilepsy-ieeg-multi Center Dataset [15]mentioning

confidence: 99%

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

2023

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More than 50 million individuals are affected by epilepsy, a chronic neurological disorder characterized by unprovoked, recurring seizures and psychological symptoms. Researchers are working to automatically detect or predict epileptic episodes through Electroencephalography (EEG) signal analysis, and machine, and deep learning methods. Good quality, open-source, and free EEG data acts as a catalyst in this ongoing battle to manage this disease. This article presents 40+ publicly available EEG datasets for adult and pediatric human populations from 2001-2023. A comparative analysis and discussion on open and private EEG datasets have been done based on objective parameters in this domain. Bonn and CHB-MIT remain the benchmark datasets used for the automatic detection of epileptic and seizure EEG signals. Meta-data has also been released for large EEG data like CHB-MIT. This article will be updated every year to report the progress and changing trends in the development of EEG datasets in this field.

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Section: Epilepsy-ieeg-multi Center Dataset [15]mentioning

confidence: 99%

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

2023

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Flexible Silver/Carbon Nanotube‐Graphene Oxide‐Polydimethylsiloxane Electrode Patch for Electroencephalography Language

Wang

et al. 2023

Advanced Intelligent Systems

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A flexible silver/carbon nanotube‐graphene oxide‐polydimethylsiloxane (Ag/CNT‐GO‐PDMS) patch electrode for recording electroencephalography (EEG) signals and recognizing words is prepared. These patches record EEG signals under the synergistic sensing mechanism of the noncontact capacitance mode of the CNT‐GO‐PDMS patch and contact current mode of the Ag claws, with low scalp contact resistance of 6.4 kΩ. In the occipital region, the signal‐to‐noise ratios (SNR) are ≈90 dB for α‐waves and 9 dB for visual‐evoked signals; the SNR of auditory‐evoked EEG signals in the temporal region is ≈10 dB. The EEG cap comprises seven Ag/CNT‐GO‐PDMS patches to record EEG signals in steady‐state visual‐evoked potentials (SSVEP) and multiple auditory steady‐state response (MASSR). These patches can recognize nine words (“one” to “nine”) in the SSVEP–MASSR paradigm, with a visual accuracy of 90.4% and auditory accuracy of 54.0%. The statistical analysis also shows that the stimulation frequency and scalp channel are significant influencing factors for the accuracy of word recognition. We developed a standardized process of flexible Ag/CNT‐GO‐PDMS patches and herein propose a new strategy to identify words, which is of great significance for the establishment of the EEG language database and the application of EEG in the field of information transmission.

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EPIC: Annotated epileptic EEG independent components for artifact reduction

et al. 2022

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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.

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EPIC: Annotated epileptic EEG independent components for artifact reduction

Cited by 3 publications

References 38 publications

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

Flexible Silver/Carbon Nanotube‐Graphene Oxide‐Polydimethylsiloxane Electrode Patch for Electroencephalography Language

EPIC: Annotated epileptic EEG independent components for artifact reduction

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