[HTML][HTML] EEG-based emotion recognition: Review of commercial EEG devices and machine learning techniques
Emotion recognition based on electroencephalography (EEG) signal features is now one of
the booming big data research areas. As the number of commercial EEG devices in the …
the booming big data research areas. As the number of commercial EEG devices in the …
Identification and removal of physiological artifacts from electroencephalogram signals: A review
Electroencephalogram (EEG), boasting the advantages of portability, low cost, and
hightemporal resolution, is a non-invasive brain-imaging modality that can be used to …
hightemporal resolution, is a non-invasive brain-imaging modality that can be used to …
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 …
EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation
Stroke is the second foremost cause of death worldwide and is one of the most common
causes of disability. Several approaches have been proposed to manage stroke patient …
causes of disability. Several approaches have been proposed to manage stroke patient …
Complexity of EEG dynamics for early diagnosis of Alzheimer's disease using permutation entropy neuromarker
Background and objective Electroencephalogram (EEG) is one of the most demanded
screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain …
screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain …
Unsupervised eye blink artifact detection from EEG with Gaussian mixture model
J Cao, L Chen, D Hu, F Dong, T Jiang… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Eye blink is one of the most common artifacts in electroencephalogram (EEG) and
significantly affects the performance of the EEG related applications, such as epilepsy …
significantly affects the performance of the EEG related applications, such as epilepsy …
Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis
Stroke survivors are more prone to developing cognitive impairment and dementia.
Dementia detection is a challenge for supporting personalized healthcare. This study …
Dementia detection is a challenge for supporting personalized healthcare. This study …
Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks
Brain–computer interface (BCI) systems are becoming increasingly popular nowadays.
Electroencephalogram (EEG) signals recorded by BCI systems are however frequently …
Electroencephalogram (EEG) signals recorded by BCI systems are however frequently …
Wavelets for EEG analysis
N Bajaj - Wavelet theory, 2020 - books.google.com
This chapter introduces the applications of wavelet for Electroencephalogram (EEG) signal
analysis. First, the overview of EEG signal is discussed to the recording of raw EEG and …
analysis. First, the overview of EEG signal is discussed to the recording of raw EEG and …
ICA With CWT and k-means for Eye-Blink Artifact Removal From Fewer Channel EEG
AK Maddirala, KC Veluvolu - IEEE Transactions on Neural …, 2022 - ieeexplore.ieee.org
In recent years, there has been an increase in the usage of consumer based EEG devices
with fewer channel configuration. Although independent component analysis has been a …
with fewer channel configuration. Although independent component analysis has been a …