EEG based emotion recognition: A tutorial and review
Emotion recognition technology through analyzing the EEG signal is currently an essential
concept in Artificial Intelligence and holds great potential in emotional health care, human …
concept in Artificial Intelligence and holds great potential in emotional health care, human …
Decoding covert speech from EEG-a comprehensive review
Over the past decade, many researchers have come up with different implementations of
systems for decoding covert or imagined speech from EEG (electroencephalogram). They …
systems for decoding covert or imagined speech from EEG (electroencephalogram). They …
A deep transfer convolutional neural network framework for EEG signal classification
Nowadays, motor imagery (MI) electroencephalogram (EEG) signal classification has
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
become a hotspot in the research field of brain computer interface (BCI). More recently, deep …
Emotion recognition from multi-channel EEG data through convolutional recurrent neural network
Automatic emotion recognition based on multi-channel neurophysiological signals, as a
challenging pattern recognition task, is becoming an important computer-aided method for …
challenging pattern recognition task, is becoming an important computer-aided method for …
EEG signals classification using the K-means clustering and a multilayer perceptron neural network model
We introduced a multilayer perceptron neural network (MLPNN) based classification model
as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were …
as a diagnostic decision support mechanism in the epilepsy treatment. EEG signals were …
A decision support system for automated identification of sleep stages from single-channel EEG signals
A decision support system for automated detection of sleep stages can alleviate the burden
of medical professionals of manually annotating a large bulk of data, expedite sleep disorder …
of medical professionals of manually annotating a large bulk of data, expedite sleep disorder …
EEG signal classification using wavelet feature extraction and a mixture of expert model
A Subasi - Expert Systems with Applications, 2007 - Elsevier
Mixture of experts (ME) is modular neural network architecture for supervised learning. A
double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME …
double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME …
A temporal–spatial deep learning approach for driver distraction detection based on EEG signals
Distracted driving has been recognized as a major challenge to traffic safety improvement.
This article presents a novel driving distraction detection method that is based on a new …
This article presents a novel driving distraction detection method that is based on a new …
Using EEG spectral components to assess algorithms for detecting fatigue
BT Jap, S Lal, P Fischer, E Bekiaris - Expert Systems with Applications, 2009 - Elsevier
Fatigue is a constant occupational hazard for drivers and it greatly reduces efficiency and
performance when one persists in continuing the current activity. Studies have investigated …
performance when one persists in continuing the current activity. Studies have investigated …
Convolutional neural network for drowsiness detection using EEG signals
Drowsiness detection (DD) has become a relevant area of active research in biomedical
signal processing. Recently, various deep learning (DL) researches based on the EEG …
signal processing. Recently, various deep learning (DL) researches based on the EEG …