Generative Adversarial Network for Image Reconstruction from Generative Adversarial Network for Image Reconstruction from Human Brain Activity. Human Brain Activity.

Brain-to-Image Generation: Reconstructing Visual Stimuli from EEG using Generative Adversarial Networks

Reconstructing visual stimuli from electroencephalography (EEG) is difficult because scalp measurements have high temporal but limited spatial resolution, and paired EEG-image datasets remain small relative to modern generative-model training corpora. We present a reproducible single-subject baseline on THINGS-EEG2 tha...

Deep Convolutional Generative Adversial Network on MNIST Dataset

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised lea...

Attention-based Harmony Generative Adversarial Network for Synthetic Brain MRI Generation and Data Augmentation in Alzheimer's Disease Analysis

Two-stage GAN with Attention Gates for Brain MRI Inpainting: A Hybrid Framework for Preserving Diagnostic Features in Alzheimer's Disease Classification

Clinical brain MRI scans for Alzheimer's disease diagnosis often suffer from motion artifacts, signal dropout, and incomplete acquisitions. While deep learning methods like LaMa produce visually coherent inpainting, they may alter critical anatomical structures, and traditional methods such as OpenCV Telea maintain loc...

Visual Stimulus Reconstruction from EEG Signals Using Masked Variational Autoencoders and Stable Diffusion

Electroencephalography (EEG)-based Brain–Computer Interfaces (BCIs) have emerged as a promising paradigm for decoding neural activity and translating brain signals into meaningful outputs. Among the various applications of BCIs, reconstructing or generating visual stimuli from neural signals represents a challenging an...

Implementation and Comparative Analysis of Magnetic Resonance Image (MRI) Reconstruction Using Generative Adversarial Networks (GANs)

Magnetic resonance imaging (MRI) scans tend to be long procedures, making it difficult for patients to remain motionless. Generative adversarial networks (GANs) are artificial intelligence models widely used in image processing. This paper addresses the use of GANs to aid in the reconstruction of magnetic resonance ima...