TL;DR
The results demonstrate that CSMFEN can effectively learn discriminative representations and achieve competitive emotion recognition performance on both datasets.
Emotion recognition is an important component for enabling machines to perceive and respond to human emotions. Existing electroencephalogram (EEG)-based emotion recognition methods often rely on single-band signals or purely spatial representations, which may fail to capture complementary spectral information and left-...
A progressive hybrid framework that integrates deep spatio-temporal learning, domain-informed feature modelling, and fuzzy adaptive reasoning for robust and fine-grained emotion recognition in the Valence–Arousal (VA) space is presented.
This paper presents an explainable CNN–Transformer framework for subject-independent EEG emotion recognition, where the CNN component learns local temporal and spatial patterns, while the Transformer encoder uses self-attention to model relationships across the learned representation.
An EEG-fNIRS emotion recognition framework based on a Spatial-Frequency Hypergraph Neural Network (SF-HGNN) that achieves high-order interaction between the complementary information of the two modalities and was validated on the public ENTER dataset.
This work proposes an approach to convert EEG signals into 2D spectrograms using Cross-wavelet transform (XWT) to feed a customized AlexNet model, which recognizes the emotions from the 2D XWT spectrograms and classifies them into respective emotion categories.
This paper presents an innovative method for recognizing emotion from EEG data from the left hemisphere of the prefrontal cortex. A hybrid deep learning model using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) is proposed. The DEAP dataset and custom dataset (MindData) used in this paper contai...
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