Emotion recognition in EEG signals using deep learning methods: A review
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …
planning, reasoning, and other mental states. As a result, they are considered a significant …
Finger pinching and imagination classification: A fusion of CNN architectures for IoMT-enabled BCI applications
Abstract A Brain–Computer Interface (BCI), integrated with the Internet of Medical Things
(IoMT) and based on electroencephalogram (EEG) technology, allows users to control …
(IoMT) and based on electroencephalogram (EEG) technology, allows users to control …
Multiple classification of brain MRI autism spectrum disorder by age and gender using deep learning
The fact that the rapid and definitive diagnosis of autism cannot be made today and that
autism cannot be treated provides an impetus to look into novel technological solutions. To …
autism cannot be treated provides an impetus to look into novel technological solutions. To …
A transformer-embedded multi-task model for dose distribution prediction
Radiation therapy is a fundamental cancer treatment in the clinic. However, to satisfy the
clinical requirements, radiologists have to iteratively adjust the radiotherapy plan based on …
clinical requirements, radiologists have to iteratively adjust the radiotherapy plan based on …
Encoder–decoder with pyramid region attention for pixel‐level pavement crack recognition
Timely and accurate extraction of pavement crack information is crucial to maintain service
conditions and structural safety for infrastructures and reduce further road maintenance …
conditions and structural safety for infrastructures and reduce further road maintenance …
A multiple frequency bands parallel spatial–temporal 3D deep residual learning framework for EEG-based emotion recognition
M Miao, L Zheng, B Xu, Z Yang, W Hu - Biomedical Signal Processing and …, 2023 - Elsevier
Electroencephalography (EEG) based emotion recognition has become a hot research
issue in the field of cognitive interaction and brain-computer interface (BCI). How to build a …
issue in the field of cognitive interaction and brain-computer interface (BCI). How to build a …
S-LSTM-ATT: a hybrid deep learning approach with optimized features for emotion recognition in electroencephalogram
Purpose Human emotion recognition using electroencephalograms (EEG) is a critical area
of research in human–machine interfaces. Furthermore, EEG data are convoluted and …
of research in human–machine interfaces. Furthermore, EEG data are convoluted and …
Attention-based convolutional recurrent deep neural networks for the prediction of response to repetitive transcranial magnetic stimulation for major depressive …
Repetitive Transcranial Magnetic Stimulation (rTMS) is proposed as an effective treatment
for major depressive disorder (MDD). However, because of the suboptimal treatment …
for major depressive disorder (MDD). However, because of the suboptimal treatment …
Facial expression recognition with contrastive learning and uncertainty-guided relabeling
Y Yang, L Hu, C Zu, Q Zhou, X Wu, J Zhou… - International Journal of …, 2023 - World Scientific
Facial expression recognition (FER) plays a vital role in the field of human-computer
interaction. To achieve automatic FER, various approaches based on deep learning (DL) …
interaction. To achieve automatic FER, various approaches based on deep learning (DL) …
Effect of action units, viewpoint and immersion on emotion recognition using dynamic virtual faces
MA Vicente-Querol, A Fernández-Caballero… - … Journal of Neural …, 2023 - World Scientific
Facial affect recognition is a critical skill in human interactions that is often impaired in
psychiatric disorders. To address this challenge, tests have been developed to measure and …
psychiatric disorders. To address this challenge, tests have been developed to measure and …