A review on nonlinear methods using electroencephalographic recordings for emotion recognition
B García-Martínez, A Martinez-Rodrigo… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Electroencephalographic (EEG) recordings are receiving growing attention in the field of
emotion recognition, since they monitor the brain's first response to an external stimulus …
emotion recognition, since they monitor the brain's first response to an external stimulus …
Taxonomy on EEG artifacts removal methods, issues, and healthcare applications
Electroencephalogram (EEG) signals are progressively growing data widely known as
biomedical big data, which is applied in biomedical and healthcare research. The …
biomedical big data, which is applied in biomedical and healthcare research. The …
Automatic eyeblink and muscular artifact detection and removal from EEG signals using k-nearest neighbor classifier and long short-term memory networks
Electroencephalogram (EEG) is often corrupted with artifacts originating from sources such
as eyes and muscles. Hybrid artifact removal methods often require human intervention for …
as eyes and muscles. Hybrid artifact removal methods often require human intervention for …
A deep learning approach in automated detection of schizophrenia using scalogram images of EEG signals
This study presents a method with high accuracy performance that aims to automatically
detect schizophrenia (SZ) from electroencephalography (EEG) records. Unlike related …
detect schizophrenia (SZ) from electroencephalography (EEG) records. Unlike related …
Autism spectrum disorder diagnostic system using HOS bispectrum with EEG signals
Autistic individuals often have difficulties expressing or controlling emotions and have poor
eye contact, among other symptoms. The prevalence of autism is increasing globally, posing …
eye contact, among other symptoms. The prevalence of autism is increasing globally, posing …
Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals
The aim of this paper is to introduce a novel method using short-term EEG signals to
separate depressed patients from healthy controls. Five common frequency bands (delta …
separate depressed patients from healthy controls. Five common frequency bands (delta …
Portable drowsiness detection through use of a prefrontal single-channel electroencephalogram
M Ogino, Y Mitsukura - Sensors, 2018 - mdpi.com
Drowsiness detection has been studied in the context of evaluating products, assessing
driver alertness, and managing office environments. Drowsiness level can be readily …
driver alertness, and managing office environments. Drowsiness level can be readily …
An EEG based channel optimized classification approach for autism spectrum disorder
D Haputhanthri, G Brihadiswaran… - 2019 Moratuwa …, 2019 - ieeexplore.ieee.org
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition which affects a
person's cognition and behaviour. It is a lifelong condition which cannot be cured completely …
person's cognition and behaviour. It is a lifelong condition which cannot be cured completely …
[HTML][HTML] The effect of music listening on EEG functional connectivity of brain: a short-duration and long-duration study
Music is considered a powerful brain stimulus, as listening to it can activate several brain
networks. Music of different kinds and genres may have a different effect on the human …
networks. Music of different kinds and genres may have a different effect on the human …
A combination of statistical parameters for the detection of epilepsy and EEG classification using ANN and KNN classifier
Electrical activity of the brain reads through the technique called as electroencephalography
for brain disorder like epilepsy. Epileptical signal is extracted from EEG signal through …
for brain disorder like epilepsy. Epileptical signal is extracted from EEG signal through …