Recognition of human emotions using EEG signals: A review
Assessment of the cognitive functions and state of clinical subjects is an important aspect of
e-health care delivery, and in the development of novel human-machine interfaces. A …
e-health care delivery, and in the development of novel human-machine interfaces. A …
A comprehensive survey on emotion recognition based on electroencephalograph (EEG) signals
K Kamble, J Sengupta - Multimedia Tools and Applications, 2023 - Springer
Emotion recognition using electroencephalography (EEG) is becoming an interesting topic
among researchers. It has made a remarkable entry in the domain of biomedical, smart …
among researchers. It has made a remarkable entry in the domain of biomedical, smart …
EEG conformer: Convolutional transformer for EEG decoding and visualization
Due to the limited perceptual field, convolutional neural networks (CNN) only extract local
temporal features and may fail to capture long-term dependencies for EEG decoding. In this …
temporal features and may fail to capture long-term dependencies for EEG decoding. In this …
A deep learning based approach for automatic detection of COVID-19 cases using chest X-ray images
In this global pandemic situation of coronavirus disease (COVID-19), it is of foremost priority
to look up efficient and faster diagnosis methods for reducing the transmission rate of the …
to look up efficient and faster diagnosis methods for reducing the transmission rate of the …
Emotion recognition from EEG based on multi-task learning with capsule network and attention mechanism
Deep learning (DL) technologies have recently shown great potential in emotion recognition
based on electroencephalography (EEG). However, existing DL-based EEG emotion …
based on electroencephalography (EEG). However, existing DL-based EEG emotion …
EEG-based cross-subject emotion recognition using Fourier-Bessel series expansion based empirical wavelet transform and NCA feature selection method
Automated emotion recognition using brain electroencephalogram (EEG) signals is
predominantly used for the accurate assessment of human actions as compared to facial …
predominantly used for the accurate assessment of human actions as compared to facial …
Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals
Emotion is interpreted as a psycho-physiological process, and it is associated with
personality, behavior, motivation, and character of a person. The objective of affective …
personality, behavior, motivation, and character of a person. The objective of affective …
Human emotion recognition based on time–frequency analysis of multivariate EEG signal
V Padhmashree, A Bhattacharyya - Knowledge-Based Systems, 2022 - Elsevier
Understanding the expression of human emotional states plays a prominent role in
interactive multimodal interfaces, affective computing, and the healthcare sector. Emotion …
interactive multimodal interfaces, affective computing, and the healthcare sector. Emotion …
Epileptic-seizure classification using phase-space representation of FBSE-EWT based EEG sub-band signals and ensemble learners
Electroencephalogram (EEG) signals are non-linear and non-stationary in nature. The
phase-space representation (PSR) method is useful for analysing the non-linear …
phase-space representation (PSR) method is useful for analysing the non-linear …
Ensemble machine learning-based affective computing for emotion recognition using dual-decomposed EEG signals
KS Kamble, J Sengupta - IEEE Sensors Journal, 2021 - ieeexplore.ieee.org
Machine learning (ML)-based algorithms have shown promising results in
electroencephalogram (EEG)-based emotion recognition. This study compares five …
electroencephalogram (EEG)-based emotion recognition. This study compares five …