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 …
A review on semi-supervised learning for EEG-based emotion recognition
Semisupervised learning holds significant academic and practical importance in the realm of
EEG-based emotion recognition. Currently, a multitude of research endeavors are dedicated …
EEG-based emotion recognition. Currently, a multitude of research endeavors are dedicated …
EEG-based emotion recognition for hearing impaired and normal individuals with residual feature pyramids network based on time–frequency–spatial features
F Hou, J Liu, Z Bai, Z Yang, J Liu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the development of affective computing, discriminative feature selection is critical for
electroencephalography (EEG) emotion recognition. In this article, we fused four EEG …
electroencephalography (EEG) emotion recognition. In this article, we fused four EEG …
TFCNN-BiGRU with self-attention mechanism for automatic human emotion recognition using multi-channel EEG data
Electroencephalograms (EEG)-based technology for recognizing emotions has attracted a
lot of interest lately. However, there is still work to be done on the efficient fusion of different …
lot of interest lately. However, there is still work to be done on the efficient fusion of different …
Convolutional gated recurrent unit-driven multidimensional dynamic graph neural network for subject-independent emotion recognition
W Guo, Y Wang - Expert Systems with Applications, 2024 - Elsevier
Electroencephalogram (EEG) could directly reflect human brain activities. Recently, EEG-
based emotion recognition technology has attracted widespread attention. However …
based emotion recognition technology has attracted widespread attention. However …
[HTML][HTML] Graph adaptive semi-supervised discriminative subspace learning for EEG emotion recognition
Since Electroencephalogram (EEG) is resistant to camouflage and contains abundant
neurophysiological information, it shows significant superiorities in objective emotion …
neurophysiological information, it shows significant superiorities in objective emotion …
[HTML][HTML] Lightweight attention mechanisms for EEG emotion recognition for brain computer interface
NK Gunda, MI Khalaf, S Bhatnagar, A Quraishi… - Journal of Neuroscience …, 2024 - Elsevier
Background In the realm of brain-computer interfaces (BCI), identifying emotions from
electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the …
electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the …
itimes: Investigating semisupervised time series classification via irregular time sampling
Semi-supervised learning (SSL) provides a powerful paradigm to mitigate the reliance on
large labeled data by leveraging unlabeled data during model training. However, for time …
large labeled data by leveraging unlabeled data during model training. However, for time …
[PDF][PDF] Semi-supervised regression with adaptive graph learning for EEG-based emotion recognition
Electroencephalogram (EEG) signals are widely used in the field of emotion recognition
since it is resistant to camouflage and contains abundant physiological information …
since it is resistant to camouflage and contains abundant physiological information …
[HTML][HTML] Orthogonal semi-supervised regression with adaptive label dragging for cross-session EEG emotion recognition
T Sha, Y Peng - Journal of King Saud University-Computer and …, 2023 - Elsevier
Owning to its merits of great temporal resolution, portability and low cost,
electroencephalogram (EEG) signals have received increasing attention in emotion …
electroencephalogram (EEG) signals have received increasing attention in emotion …