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Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model
Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention.
Subject-Independent Deep Architecture for EEG-based Motor Imagery Classification
Second, a supervised part learns a classifier based on the labeled training samples using the latent features acquired in the unsupervised part.
Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition
Although deep learning-based algorithms have demonstrated excellent performance in automated emotion recognition via electroencephalogram (EEG) signals, variations across brain signal patterns of individuals can diminish the model's effectiveness when applied across different subjects.
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition
Using DEAP as the source dataset, we demonstrate the effectiveness of our model in performing cross-modality TL and improving emotion classification accuracy on DREAMER and the Emotional English Word (EEWD) datasets, which involve EEG-based emotion classification tasks with different stimuli.
Manifold Regularization for Memory-Efficient Training of Deep Neural Networks
Use of the framework results in improved absolute performance and empirical generalization error relative to traditional learning techniques.
