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EVNCERS: An integrated eigenvector centrality-variational nonlinear chirp mode decomposition-based EEG rhythm separation for automatic emotion recognition
Affective computing, which focuses on identifying emotions from physiological data, namely
electroencephalography (EEG) is becoming increasingly significant. However, direct …
electroencephalography (EEG) is becoming increasingly significant. However, direct …
Emotion recognition using wavelet synchrosqueezing transform integrated with ensemble deep learning
This study designs a wavelet-based synchrosqueezing transform (WSST)-driven optimized
ensemble deep learning (DL)-based automatic multiclass emotion recognition (AMER) …
ensemble deep learning (DL)-based automatic multiclass emotion recognition (AMER) …
A novel variational nonlinear chirp mode decomposition-based critical brain-region investigation for automatic emotion recognition
The field of affective computing that deals with emotion recognition from physiological
information, particularly electroencephalography (EEG), is becoming more and more …
information, particularly electroencephalography (EEG), is becoming more and more …
[HTML][HTML] The efficacy and utility of lower-dimensional Riemannian geometry for EEG-based emotion classification
Electroencephalography (EEG) signals have diverse applications in brain-computer
interfaces (BCIs), neurological condition diagnoses, and emotion recognition across …
interfaces (BCIs), neurological condition diagnoses, and emotion recognition across …
CycleMVAE: Benchmarking end-to-end cycle-consistent multi-task variational autoencoder for EEG-based emotion recognition
Affective computing, particularly the identification of emotions from multichannel
electroencephalography (EEG) signals, has gained importance. In this study, we propose a …
electroencephalography (EEG) signals, has gained importance. In this study, we propose a …
Emotion recognition using machine learning models on electroencephalogram (eeg) data
S Asha, S Roshini, K Vignesh - 2023 Second International …, 2023 - ieeexplore.ieee.org
Analyzing Electroencephalogram (EEG) data using cutting-edge machine learning
techniques to comprehend emotional states. EEGs are non-invasive and have been shown …
techniques to comprehend emotional states. EEGs are non-invasive and have been shown …
Advancing multimodal emotion analysis: integrating machine learning and deep learning approaches
Y Zhu - International Conference on Electronics, Electrical and …, 2024 - spiedigitallibrary.org
Multimodal emotion analysis, blending machine learning and deep learning, is transforming
computer-based human emotion recognition. This review examines the complexity of human …
computer-based human emotion recognition. This review examines the complexity of human …
[PDF][PDF] Overview of Current Trends in Machine Learning Approaches for EEG-Based Brain Computer Interface Applications
AY Ferdi, A Ghazli - 2024 - ceur-ws.org
The potential of the human brain to communicate and interact with the environment is
promoted by advances in neuroscience and computer science, making brain-computer …
promoted by advances in neuroscience and computer science, making brain-computer …