Data augmentation for EEG-based emotion recognition with deep convolutional neural networks

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Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA

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A Deep Learning Approach for Motor Imagery EEG Signal Classification

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Real-time EEG-based emotion recognition for music therapy

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EEG-based biometric identification with deep learning

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Towards wireless emotional valence detection from EEG

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Affective state recognition from EEG with deep belief networks

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The novel recognition method with Optimal Wavelet Packet and LSTM based Recurrent Neural Network

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Table of Contents

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EEG Based Emotion Recognition: A Tutorial and Review

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Data augmentation for deep-learning-based electroencephalography

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HS-CNN: a CNN with hybrid convolution scale for EEG motor imagery classification

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Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

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Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

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Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment

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EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification

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An efficient multi-scale CNN model with intrinsic feature integration for motor imagery EEG subject classification in brain-machine interfaces

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Learning CNN features from DE features for EEG-based emotion recognition

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Data augmentation for enhancing EEG-based emotion recognition with deep generative models.

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GANSER: A Self-Supervised Data Augmentation Framework for EEG-Based Emotion Recognition

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Data augmentation for motor imagery signal classification based on a hybrid neural network.

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Cross-Dataset Variability Problem in EEG Decoding With Deep Learning

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Data augmentation for learning predictive models on EEG: a systematic comparison

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Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review

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EEG-based emotion recognition systems; comprehensive study

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EEG Feature Extraction and Data Augmentation in Emotion Recognition

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A Survey on Deep Learning-Based Short/Zero-Calibration Approaches for EEG-Based Brain–Computer Interfaces

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Data Augmentation for EEG-Based Emotion Recognition Using Generative Adversarial Networks

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Glioma Brain Tumor Segmentation in Four MRI Modalities Using a Convolutional Neural Network and Based on a Transfer Learning Method

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Deep Learning Methods for Multi-Channel EEG-Based Emotion Recognition

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Robust learning from corrupted EEG with dynamic spatial filtering

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Constructing an Emotion Estimation Model Based on EEG/HRV Indexes Using Feature Extraction and Feature Selection Algorithms

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Wavelet ELM-AE Based Data Augmentation and Deep Learning for Efficient Emotion Recognition Using EEG Recordings

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fNIRS-GANs: data augmentation using generative adversarial networks for classifying motor tasks from functional near-infrared spectroscopy

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MIEEG-GAN: Generating Artificial Motor Imagery Electroencephalography Signals

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Object classification from randomized EEG trials

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A Graph Neural Network for EEG-Based Emotion Recognition With Contrastive Learning and Generative Adversarial Neural Network Data Augmentation

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A GAN-Based Data Augmentation Method for Multimodal Emotion Recognition

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Feasibility of Detecting Potential Emergencies in Symbiotic Human-Robot Collaboration with a mobile EEG

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Time‐Frequency Analysis of EEG Signals and GLCM Features for Depth of Anesthesia Monitoring

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Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages

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BrainGridNet: A two-branch depthwise CNN for decoding EEG-based multi-class motor imagery

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Detecting Human Trust Calibration in Automation: A Convolutional Neural Network Approach

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Generative AI Enables EEG Data Augmentation for Alzheimer’s Disease Detection Via Diffusion Model

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Cross-Channel Specific-Mutual Feature Transfer Learning for Motor Imagery EEG Signals Decoding

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Channel reflection: Knowledge-driven data augmentation for EEG-based brain–computer interfaces

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Beyond Mimicking Under-Represented Emotions: Deep Data Augmentation with Emotional Subspace Constraints for EEG-Based Emotion Recognition

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Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

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Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals

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SSVEP-Based Emotion Recognition for IoT via Multiobjective Neural Architecture Search

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Construction and Analysis of Emotion Computing Model Based on LSTM

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Generative AI with WGAN-GP for boosting seizure detection accuracy

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Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning

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A Weighted Co-Training Framework for Emotion Recognition Based on EEG Data Generation Using Frequency-Spatial Diffusion Transformer

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Decoding motor imagery tasks using ESI and hybrid feature CNN

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Novel high-dimensional phase space features for EEG emotion recognition

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Data Augmentation Effects on Highly Imbalanced EEG Datasets for Automatic Detection of Photoparoxysmal Responses

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A Novel Spatio-Temporal Field for Emotion Recognition Based on EEG Signals

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Decoding human taste perception by reconstructing and mining temporal-spatial features of taste-related EEGs

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Decoding visual motions from EEG using attention based RNN.

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From time series to image analysis: A transfer learning approach for night setback identification of district heating substations

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Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks

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Classify Motor Imagery by a Novel CNN with Data Augmentation

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Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis

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Convolutional neural networks for decoding electroencephalography responses and visualizing trial by trial changes in discriminant features

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Identifying stable EEG patterns over time for mental workload recognition using transfer DS-CNN framework

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Synthesizing labeled data to enhance soft sensor performance in data-scarce regions

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Innovative Poincare’s plot asymmetry descriptors for EEG emotion recognition

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Recognizing the Damaged Surface Parts of Cars in the Real Scene Using a Deep Learning Framework

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Data augmentation for enhancing EEG-based emotion recognition with deep generative models.

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Improving Automatic Recognition of Emotional States Using EEG Data Augmentation Techniques

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EEG Feature Extraction and Data Augmentation in Emotion Recognition

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An Advanced Data Augmentation Scheme on Limited EEG Signals for Human Emotion Recognition

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EEG Data Augmentation for Emotion Recognition Using Diffusion Model

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