Removal of artifacts from EEG signals: a review
Electroencephalogram (EEG) plays an important role in identifying brain activity and
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
Early diagnosis of Alzheimer's disease: the role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts
PM Rossini, R Di Iorio, F Vecchio, M Anfossi… - Clinical …, 2020 - Elsevier
Alzheimer's disease (AD) is the most common neurodegenerative disease among the
elderly with a progressive decline in cognitive function significantly affecting quality of life …
elderly with a progressive decline in cognitive function significantly affecting quality of life …
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website
L Pion-Tonachini, K Kreutz-Delgado, S Makeig - NeuroImage, 2019 - Elsevier
The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and
relatively low-cost measure of mesoscale brain dynamics with high temporal resolution …
relatively low-cost measure of mesoscale brain dynamics with high temporal resolution …
The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): standardized processing software for developmental and high-artifact data
LJ Gabard-Durnam, AS Mendez Leal… - Frontiers in …, 2018 - frontiersin.org
Electroenchephalography (EEG) recordings collected with developmental populations
present particular challenges from a data processing perspective. These EEGs have a high …
present particular challenges from a data processing perspective. These EEGs have a high …
EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising
Objective. Deep learning (DL) networks are increasingly attracting attention across various
fields, including electroencephalography (EEG) signal processing. These models provide …
fields, including electroencephalography (EEG) signal processing. These models provide …
Automagic: Standardized preprocessing of big EEG data
Electroencephalography (EEG) recordings have been rarely included in large-scale studies.
This is arguably not due to a lack of information that lies in EEG recordings but mainly on …
This is arguably not due to a lack of information that lies in EEG recordings but mainly on …
Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry
Frontal electroencephalographic (EEG) alpha asymmetry is widely researched in studies of
emotion, motivation, and psychopathology, yet it is a metric that has been quantified and …
emotion, motivation, and psychopathology, yet it is a metric that has been quantified and …
[HTML][HTML] Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed
Research in cognitive neuroscience has extensively demonstrated that the temporal
dynamics of brain activity are associated with cognitive functioning. The temporal dynamics …
dynamics of brain activity are associated with cognitive functioning. The temporal dynamics …
[HTML][HTML] Analysing concurrent transcranial magnetic stimulation and electroencephalographic data: A review and introduction to the open-source TESA software
The concurrent use of transcranial magnetic stimulation with electroencephalography (TMS–
EEG) is growing in popularity as a method for assessing various cortical properties such as …
EEG) is growing in popularity as a method for assessing various cortical properties such as …
Review of challenges associated with the EEG artifact removal methods
Electroencephalography (EEG), as a non-invasive modality, enables the representation of
the underlying neuronal activities as electrical signals with high temporal resolution. In …
the underlying neuronal activities as electrical signals with high temporal resolution. In …