Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition
Y Cimtay, E Ekmekcioglu - Sensors, 2020 - mdpi.com
The electroencephalogram (EEG) has great attraction in emotion recognition studies due to
its resistance to deceptive actions of humans. This is one of the most significant advantages …
its resistance to deceptive actions of humans. This is one of the most significant advantages …
Deep learning-based approach for emotion recognition using electroencephalography (EEG) signals using bi-directional long short-term memory (Bi-LSTM)
Emotions are an essential part of daily human communication. The emotional states and
dynamics of the brain can be linked by electroencephalography (EEG) signals that can be …
dynamics of the brain can be linked by electroencephalography (EEG) signals that can be …
[PDF][PDF] A Comprehensive Review for Emotion Detection Based on EEG Signals: Challenges, Applications, and Open Issues.
A Abdulrahman, M Baykara - Traitement du Signal, 2021 - researchgate.net
Accepted: 25 July 2021 Emotion classification based on physiological signals has become a
hot topic in the past decade. Many studies have attempted to classify emotions using various …
hot topic in the past decade. Many studies have attempted to classify emotions using various …
Fault diagnosis for train plug door using weighted fractional wavelet packet decomposition energy entropy
As the passage for passengers to get on and off, train plug doors directly affect the operation
efficiency of the train and the personal safety of passengers. This paper proposes a non …
efficiency of the train and the personal safety of passengers. This paper proposes a non …
MTLFuseNet: a novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning
R Li, C Ren, Y Ge, Q Zhao, Y Yang, Y Shi… - Knowledge-Based …, 2023 - Elsevier
How to extract discriminative latent feature representations from electroencephalography
(EEG) signals and build a generalized model is a topic in EEG-based emotion recognition …
(EEG) signals and build a generalized model is a topic in EEG-based emotion recognition …
[PDF][PDF] The challenges of emotion recognition methods based on electroencephalogram signals: A literature review
Electroencephalogram (EEG) signals in recognizing emotions have several advantages.
Still, the success of this study, however, is strongly influenced by: i) the distribution of the …
Still, the success of this study, however, is strongly influenced by: i) the distribution of the …
Machine-learning-based emotion recognition system using EEG signals
R Alhalaseh, S Alasasfeh - Computers, 2020 - mdpi.com
Many scientific studies have been concerned with building an automatic system to recognize
emotions, and building such systems usually relies on brain signals. These studies have …
emotions, and building such systems usually relies on brain signals. These studies have …
[PDF][PDF] A new method for voice signal features creation
Digital audio is one of the most important types of data at present. It is used in several
applications, such as human knowledge and many security and banking applications. A …
applications, such as human knowledge and many security and banking applications. A …
Variational instance-adaptive graph for EEG emotion recognition
The individual differences and the dynamic uncertain relationships among different
electroencephalogram (EEG) regions are essential factors that limit EEG emotion …
electroencephalogram (EEG) regions are essential factors that limit EEG emotion …
Emotion recognition framework using multiple modalities for an effective human–computer interaction
Human emotions are subjective reactions to objects or events that are related to diverse
physiological, behavioral and intellectual changes. The research community is gaining more …
physiological, behavioral and intellectual changes. The research community is gaining more …