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Visual Saliency and Image Reconstruction from EEG Signals via an Effective Geometric Deep Network-Based Generative Adversarial Network

https://doi.org/10.3390/electronics11213637

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Developing an efficient functional connectivity-based geometric deep network for automatic EEG-based visual decoding

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Characterization of electroencephalography signals for estimating saliency features in videos

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Inverse retinotopy: Inferring the visual content of images from brain activation patterns

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Encoding and decoding in fMRI

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A simple method for detecting salient regions

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Encoding based saliency detection for videos and images

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Fully convolutional networks for semantic segmentation

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Deep Learning Human Mind for Automated Visual Classification

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Real-Time Deep Neuro-Vision Embedded Processing System for Saliency-based Car Driving Safety Monitoring

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Saliency Detection: A Boolean Map Approach

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SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks

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A semantic no-reference image sharpness metric based on top-down and bottom-up saliency map modeling

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Region Enhanced Scale-Invariant Saliency Detection

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EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks

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Learning Computational Models of Video Memorability from fMRI Brain Imaging

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Generalized principal component analysis (GPCA)

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Top-Down Visual Saliency via Joint CRF and Dictionary Learning

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What Do Different Evaluation Metrics Tell Us About Saliency Models?

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Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features

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Saliency detection based on BP-neural Network

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Presaccadic EEG activity predicts visual saliency in free‐viewing contour integration

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EEG Alpha Activity Reflects Attentional Demands, and Beta Activity Reflects Emotional and Cognitive Processes

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Robust subspace analysis for detecting visual attention regions in images

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ImageNet classification with deep convolutional neural networks

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Contrast-based image attention analysis by using fuzzy growing

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Object Extraction in Cluttered Environments via a P300-Based IFCE

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Moving Mirrors: A High-density EEG Study Investigating the Effect of Camera Movements on Motor Cortex Activation during Action Observation

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Rapid Natural Image Identification Based on EEG data and Global Scene Statistics

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Learning a saliency map using fixated locations in natural scenes

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Predicting human gaze beyond pixels

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EEG-based image classification via a region-level stacked bi-directional deep learning framework

inferredcites →https://doi.org/10.1523/jneurosci.3577-09.2009openalex-snapshot

Decoding and Reconstructing Color from Responses in Human Visual Cortex

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Recognition Covid-19 cases using deep type-2 fuzzy neural networks based on chest X-ray image

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Automatic Detection of Driver Fatigue Based on EEG Signals Using a Developed Deep Neural Network

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Advances In Deep Learning On Graphs (Gsp'18 Workshop)

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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EEG-based functional connectivity analysis of brain abnormalities: A systematic review study

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PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer’s disease

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EEG2Vision: High-Fidelity Image Reconstruction from EEG via Temporal-Spectral Fusion and Cross-Modal Alignment

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Cognition-Supervised Saliency Detection: Contrasting EEG Signals and Visual Stimuli

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From Neural Signals to Images: Generative Reconstruction of Visual Stimuli from Electroencephalography Signals

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Generative Adversarial Networks for Modeling Bio-Electric Fields in Medicine: A Review of EEG, ECG, EMG, and EOG Applications

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A Customized Efficient Deep Learning Model for the Diagnosis of Acute Leukemia Cells Based on Lymphocyte and Monocyte Images

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Automatic Emotion Recognition from EEG Signals Using a Combination of Type-2 Fuzzy and Deep Convolutional Networks

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Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals

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A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks

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Decoding Temporally Encoded 3D Objects from Low-Cost Wearable Electroencephalography

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