Intelligent optimal feature selection-based hybrid variational autoencoder and block recurrent transformer network for accurate emotion recognition model using EEG …
CHN Reddy, S Mahesh, K Manjunathachari - Signal, Image and Video …, 2024 - Springer
In the context of emotion recognition, Artificial Intelligence technology has demonstrated
several functions in people's lives. Computing research is now focused on …
several functions in people's lives. Computing research is now focused on …
Inventive deep convolutional neural network classifier for emotion identification in accordance with EEG signals
J Khubani, S Kulkarni - Social Network Analysis and Mining, 2023 - Springer
Emotion identification is the current research concept as it obtains a significant role in
interpersonal relationships and health care services. The electroencephalogram (EEG) is …
interpersonal relationships and health care services. The electroencephalogram (EEG) is …
Age and Gender Prediction Using Paralinguistic Features of EEG Signals and Machine Learning
R Alamr, W Alsumari, T Alotaiby - 2024 6th International …, 2024 - ieeexplore.ieee.org
The development of brain-computer interfaces (BCI) has sparked significant interest in
leveraging electroencephalography (EEG) data for diverse applications. One of the …
leveraging electroencephalography (EEG) data for diverse applications. One of the …
[CITATION][C] Graph convolutional neural network with multi-scale attention mechanism for EEG-based motion imagery classification
J Zhu, Q Liu, C Xu - International Journal of Pattern Recognition and …, 2023 - World Scientific
Recently, deep learning has been widely used in the classification of EEG signals and
achieved satisfactory results. However, the correlation between EEG electrodes is rarely …
achieved satisfactory results. However, the correlation between EEG electrodes is rarely …