{"id":4655,"date":"2025-11-11T04:10:00","date_gmt":"2025-11-11T04:10:00","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4655"},"modified":"2025-11-11T04:11:46","modified_gmt":"2025-11-11T04:11:46","slug":"cycle-consistent-adversarial-networks","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4655","title":{"rendered":"Cycle-Consistent Adversarial Networks"},"content":{"rendered":"\n<p>Training and evaluating&nbsp;GANs&nbsp;for brain activity-based image reconstruction is typically a multi-step process involving&nbsp;<mark>pre-training on large image datasets, mapping fMRI data to a latent space, and using a combination of quantitative metrics and qualitative human assessment<\/mark>.&nbsp;<\/p>\n\n\n\n<p>Training Process<\/p>\n\n\n\n<p>The training process usually involves a hybrid framework and can be broken down into three main stages:&nbsp;<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Pre-training the Generative Model:<\/strong>\n<ul class=\"wp-block-list\">\n<li>A GAN (or more stable variants like StyleGAN or DGN) is first pre-trained on a large, generic dataset of natural images (e.g., ImageNet, CelebA for faces).<\/li>\n\n\n\n<li>This step is crucial because the GAN learns the fundamental distribution and structure of real-world images, acting as a powerful &#8220;natural image prior&#8221;.<\/li>\n\n\n\n<li>The pre-trained generator network is often the only part of the GAN architecture used in the final reconstruction phase; the discriminator is often discarded after this stage.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Training the Brain Activity Decoder:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Simultaneously, a separate decoder model is trained to map brain activity (fMRI) data to the&nbsp;<em>latent space<\/em>&nbsp;of the pre-trained GAN.<\/li>\n\n\n\n<li>fMRI data is collected while subjects view or imagine images. These images&#8217; features (e.g., VGG or CLIP features) are extracted using a separate, pre-trained deep neural network.<\/li>\n\n\n\n<li>The decoder (often a linear model or another small neural network) learns the correlation between specific brain regions&#8217; activity patterns and these image features.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Fine-tuning and Optimization:<\/strong>\n<ul class=\"wp-block-list\">\n<li>In the final reconstruction phase, the process is an optimization challenge rather than a traditional, end-to-end GAN training.<\/li>\n\n\n\n<li>For a given test fMRI sample, the goal is to find the optimal latent vector (input to the generator) that produces an image whose deep features (extracted by the &#8220;comparator&#8221; DNN) best match the features decoded from the fMRI data.<\/li>\n\n\n\n<li>This optimization is guided by a&nbsp;<strong>loss function<\/strong>&nbsp;that often combines:\n<ul class=\"wp-block-list\">\n<li><strong>Feature Loss:<\/strong>&nbsp;Measures the distance between the features of the generated image and the decoded brain features in the DNN feature space (e.g., using MSE or correlation).<\/li>\n\n\n\n<li><strong>Adversarial Loss (optional):<\/strong>&nbsp;Some methods may fine-tune the GAN using an adversarial loss with the discriminator and the limited fMRI-image pairs to further refine realism for the specific subject&#8217;s data.<\/li>\n\n\n\n<li><strong>Pixel-wise Loss (optional):<\/strong>&nbsp;May include a term for direct pixel-wise similarity if ground-truth images are available during training\/validation, although this is less common for&nbsp;<em>imagined<\/em>&nbsp;images.&nbsp;<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n<p>Evaluation Metrics<\/p>\n\n\n\n<p>Evaluation involves both quantitative metrics and qualitative human judgment to assess the fidelity (realism) and diversity of the generated images.&nbsp;<\/p>\n\n\n\n<p>Quantitative Evaluation<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Pixel-level Metrics:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Mean Squared Error (MSE)&nbsp;\/&nbsp;Root Mean Square Error (RMSE):<\/strong>&nbsp;Measures average pixel intensity differences from the ground truth. Useful for initial checks but may not correlate well with human perception.<\/li>\n\n\n\n<li><strong>Peak Signal-to-Noise Ratio (PSNR):<\/strong>&nbsp;Another measure of pixel-level similarity.<\/li>\n\n\n\n<li><strong>Structural Similarity Index (SSIM):<\/strong>&nbsp;Evaluates image quality by considering luminance, contrast, and structure, aligning better with human perception than MSE\/PSNR.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Distribution &amp; Feature-level Metrics:<\/strong>\n<ul class=\"wp-block-list\">\n<li><strong>Fr\u00e9chet Inception Distance (FID):<\/strong>&nbsp;A popular metric that calculates the distance between the feature distributions of real and generated images using a pre-trained Inception network. Lower FID scores indicate better quality and diversity.<\/li>\n\n\n\n<li><strong>Inception Score (IS):<\/strong>&nbsp;Measures the clarity (fidelity) and variety (diversity) of generated images. Higher scores are better.<\/li>\n\n\n\n<li><strong>CLIP Score:<\/strong>&nbsp;In methods using CLIP, this metric measures the semantic similarity between the generated images and their corresponding text descriptions or actual target images, providing a quantitative measure of semantic content.&nbsp;<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>Qualitative Evaluation<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Human Subjective Assessment:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Researchers often employ human evaluators (sometimes via crowdsourcing platforms like&nbsp;<a href=\"https:\/\/www.mturk.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Amazon Mechanical Turk<\/a>) to rate images on realism, clarity, and resemblance to the intended target\/imagined image.<\/li>\n\n\n\n<li>Human evaluators might be asked to perform a &#8220;Turing test&#8221; &#8211; distinguishing generated images from real ones &#8211; or a &#8220;paired comparison&#8221; task to determine which of two reconstructions is better.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Visual Inspection:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Direct manual inspection by researchers is a basic, but essential, way to check for visual artifacts, mode collapse, or the overall plausibility of the generated images.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>Latest trends in&nbsp;Brain-Computer Interfaces (BCIs)&nbsp;using&nbsp;Deep Neural Networks (DNNs)&nbsp;and&nbsp;Generative Adversarial Networks (GANs)&nbsp;focus on significantly enhancing neural signal processing, decoding accuracy, and real-world application performance. Key trends include&nbsp;<mark><strong>using GANs for data augmentation<\/strong>,&nbsp;<strong>improving non-invasive BCI performance with advanced DNN architectures<\/strong>, and developing&nbsp;<strong>hybrid Brain-AI systems for shared autonomy in complex tasks<\/strong><\/mark>.&nbsp;<\/p>\n\n\n\n<p>Key Trends in BCI using DNNs and GANs<\/p>\n\n\n\n<p>1. Data Augmentation and Synthesis using GANs<\/p>\n\n\n\n<p>A significant challenge in BCI research is the scarcity of high-quality, labeled brain activity data (e.g., EEG or fMRI), which are expensive and time-consuming to collect. GANs are widely used to address this data limitation by:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Generating Synthetic Data:<\/strong>&nbsp;GANs generate synthetic, yet realistic-looking, brain signal data (e.g., EEG signals) to expand training datasets.<\/li>\n\n\n\n<li><strong>Enhancing Model Robustness:<\/strong>&nbsp;This augmentation helps train more robust and generalized DNN models that perform better across different individuals and sessions, reducing issues like overfitting.<\/li>\n\n\n\n<li><strong>Solving Mode Collapse:<\/strong>&nbsp;Researchers are developing methods to ensure the generated data is diverse and representative of real-world variations, avoiding &#8220;mode collapse&#8221; where the GAN only produces a limited variety of samples.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>2. Advanced Neural Decoding with DNNs<\/p>\n\n\n\n<p>DNNs are central to decoding complex neural activity into meaningful commands or content. Trends include:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Multi-modal AI:<\/strong>&nbsp;Combining information from various sources like images, text, and different brain signals (e.g., fMRI and EEG) to create more comprehensive understanding and control.<\/li>\n\n\n\n<li><strong>Real-time Processing:<\/strong>&nbsp;Development of energy-efficient neural networks, like Spiking Neural Networks (SNNs), to enable real-time processing and low-latency control for neuroprosthetics and other applications.<\/li>\n\n\n\n<li><strong>Novel Architectures:<\/strong>&nbsp;Utilizing sophisticated DNN architectures (e.g., Graph Convolutional Networks, recurrent neural networks with two optimization steps) that can better learn the complex, non-linear relationships between brain activity and behavior or internal states (e.g., mood).<\/li>\n\n\n\n<li><strong>Decoding Complex Information:<\/strong>&nbsp;Moving beyond simple movement commands to decoding complex information, such as the generation of unseen words from EEG signals for advanced communication aids.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>3. Hybrid Brain-AI Systems and Shared Autonomy<\/p>\n\n\n\n<p>The combination of human intention (via BCI) and AI&#8217;s capabilities for task execution is a major trend, often referred to as &#8220;shared autonomy&#8221;.&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AI &#8220;Copilots&#8221;:<\/strong>&nbsp;AI algorithms act as copilots to assist users in completing complex tasks, such as controlling a robotic arm, where the human provides high-level intent and the AI handles the fine-grained motor control.<\/li>\n\n\n\n<li><strong>Improved Non-invasive BCI:<\/strong>&nbsp;Significant efforts are being made to improve the performance of non-invasive BCIs (which are safer and more accessible) for complex tasks, closing the gap with invasive methods through advanced AI algorithms.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>4. Image Reconstruction and &#8220;Brain-to-Content&#8221;<\/p>\n\n\n\n<p>Building on the use of GANs for image generation, researchers are exploring &#8220;brain-to-content&#8221; technologies where brain activity is used to create rich media.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Realistic Image Synthesis:<\/strong>&nbsp;Using GANs and Variational Auto-Encoders (VAEs) as strong &#8220;natural image priors&#8221; to reconstruct high-quality, realistic visual experiences (images and potentially videos) from fMRI data.<\/li>\n\n\n\n<li><strong>Visualizing Mental Imagery:<\/strong>&nbsp;This trend aims to not just classify what a person is thinking, but to literally visualize their imagined images, offering new avenues for neuroscience research and potential applications in art or communication.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>5. System Stabilization and Domain Adaptation<\/p>\n\n\n\n<p>Neural recordings can change over time, requiring frequent recalibration of BCI systems. Researchers are using methods like Cycle-Consistent Adversarial Networks (Cycle-GAN) to align the distribution of neural signals across different sessions, stabilizing system performance over time with minimal user effort.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Improved image reconstruction from brain activity through automatic &#8230;Feb 9, 2025 \u2014 The trained decoder was then used to decode semantic features from the fMRI data obtained from the test sessions. Our p&#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC11353572\/#:~:text=Generative%20adversarial%20networks%20(GANs)%20have,and%20application%20within%20the%20field.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>What Radiologists Need to Know &#8211; PMC &#8211; NIHAug 12, 2024 \u2014 5. Performance Evaluation * 5.1. Quantitative Evaluation. (1) Metrics. In the field of medical imaging, several metric&#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC9992336\/#:~:text=2.3.&amp;text=A%20set%20of%20metrics%20has,of%20generated%20and%20real%20data.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Applications of generative adversarial networks in neuroimaging &#8230;2.3. &#8230; A set of metrics has been used for evaluating the quality of data generated by GAN-based models. Mean square error (MSE),<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/towardsdatascience.com\/on-the-evaluation-of-generative-adversarial-networks-b056ddcdfd3a\/#:~:text=The%20most%20common%20metrics%20to,diversity%20(variety%20of%20images).\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>On the evaluation of Generative Adversarial NetworksApr 6, 2022 \u2014 When measuring how well our GAN performs we need to evaluate two main properties: * Fidelity: the quality of the genera&#8230;<img data-opt-id=1162973713  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/973aee72-1177-44e5-a61d-b922b4e3f0e3\" alt=\"\">Towards Data Science<img data-opt-id=72152572  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/53422a41-1dfc-4276-94bf-27edfc82e17e\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC7954952\/#:~:text=Abstract,were%20conducted%20with%20test%20images.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Deep Natural Image Reconstruction from Human Brain Activity &#8230;Nov 21, 2020 \u2014 Abstract. Brain decoding based on functional magnetic resonance imaging has recently enabled the identification of vis&#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/html\/2312.07478v1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Double-Flow GAN model for the reconstruction of perceived &#8230;3.4 The training Pipeline * The whole training process are divided into three steps, 1) training the fMRI projector; 2) pretrainin&#8230;<img data-opt-id=1246795576  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/cf20b2ce-6da8-48e1-93df-61199382b3c0\" alt=\"\">arXiv<\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC6529435\/#:~:text=The%20Generator%20network%20learns%20to,%2DD%20latent%20vector%20estimate).\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Reconstructing faces from fMRI patterns using deep generative &#8230; &#8211; NIHThe Generator network learns to convert latent 1024-D vectors from the latent space into plausible face images. The Discriminator &#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=YrO1v7-KcXs&amp;t=1\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Deep image reconstruction from human brain activity (Paper &#8230;May 24, 2020 \u2014 hi there today we&#8217;re looking at deep image reconstruction. from human brain activity by Guawa Shen tommoyasu horicima &#8230;<img data-opt-id=577062158  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/d1a9b597-831e-4fd8-91d3-c5eddaa974ad\" alt=\"\"><img data-opt-id=1421836462  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/6368fe35-e132-44f9-afd1-90abbf74491d\" alt=\"\">YouTube\u00b7Yannic Kilcher<img data-opt-id=656153262  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/389f72b4-53fc-426d-990d-d3053bf84d1a\" alt=\"\">1m<\/li>\n\n\n\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Fr%C3%A9chet_inception_distance#:~:text=The%20Fr%C3%A9chet%20inception%20distance%20(FID,%22ground%20truth%22%20set).\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Fr\u00e9chet inception distance &#8211; WikipediaThe Fr\u00e9chet inception distance (FID) is a metric used to assess the quality of images created by a generative model, like a genera&#8230;<img data-opt-id=385329154  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/beba5f03-aa17-41ba-80c5-d63f517b0485\" alt=\"\">Wikipedia<\/li>\n\n\n\n<li><a href=\"https:\/\/www.youtube.com\/watch?v=O6J9zdbL7NM&amp;t=232\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Synthesizing medical images using generative adversarial &#8230;Sep 14, 2020 \u2014 if you don&#8217;t know it may not be helpful but I&#8217;ll do it. anyway. so GANs are and sort of other kinds of implicit genera&#8230;<img data-opt-id=1323465262  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/76795229-c35a-47ea-bf40-dd960a26dceb\" alt=\"\"><img data-opt-id=449405935  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/c91603d2-0d00-4547-a2d3-ca04cd0afc81\" alt=\"\">YouTube\u00b7Institute for Advanced Study<img data-opt-id=1685125002  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/dcce302d-09e8-4bfd-8198-0051825a703a\" alt=\"\">3m<\/li>\n\n\n\n<li><a href=\"https:\/\/machinelearningmastery.com\/how-to-implement-the-inception-score-from-scratch-for-evaluating-generated-images\/#:~:text=In%20the%20paper%2C%20the%20authors%20use%20a,a%20large%20number%20of%20GAN%20generated%20images.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>How to Implement the Inception Score (IS) for Evaluating GANsIn the paper, the authors use a crowd-sourcing platform (Amazon Mechanical Turk) to evaluate a large number of GAN generated image&#8230;<img data-opt-id=845815013  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/16efb879-355c-4392-921f-363e491ce214\" alt=\"\">Machine Learning Mastery<img data-opt-id=1116628601  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/7afcf3bd-fd33-4e43-8d60-221f0de2ae4d\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/html\/2410.17932v1#:~:text=The%20system%20is%20trained%20end%2Dto%2Dend%20in%20its,via%20gradient%20descent%20from%20the%20input%20images.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>VR-Splatting: Foveated Radiance Field Rendering via 3D Gaussian Splatting and Neural PointsOct 22, 2024\u00a0\u2014\u00a0The system is trained end-to-end in its hybrid form. Parameters of Gaussians and neural points are optimized via gradi&#8230;<img data-opt-id=1070120962  decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/arxiv.org&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">arXiv<\/li>\n\n\n\n<li><a href=\"https:\/\/k21academy.com\/ai-ml\/gen-ai\/generative-adversarial-networks\/#:~:text=Generative%20Adversarial%20Networks%20(GANs)%20are%20evaluated%20using,by%20comparing%20them%20to%20real%20data%20distributions.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Generative Adversarial Network (GAN)Nov 28, 2024\u00a0\u2014\u00a0Generative Adversarial Networks (GANs) are evaluated using a combination of qualitative and quantitative methods. Comm&#8230;<img data-opt-id=1070120962  decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/k21academy.com&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">K21 Academy<img data-opt-id=192477137  decoding=\"async\" src=\"https:\/\/encrypted-tbn3.gstatic.com\/images?q=tbn:ANd9GcRtai5PPNMdv_a_cGCDe77XyTcPxiJUfC8451njBQDPrY9XD768\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10819962\/#:~:text=To%20assess%20the%20effectiveness%20of%20the%20data,through%20the%20examination%20of%20the%20generated%20samples.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Generative Adversarial Network-Based Data Augmentation for Enhancing Wireless Physical Layer AuthenticationJan 18, 2024\u00a0\u2014\u00a0To assess the effectiveness of the data augmentation GAN-based model, we split the process of evaluation into two part&#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/www.ultihash.io\/gans#:~:text=GANs%20need%20large%20training%20datasets%2C%20often%20consisting,of%20the%20neural%20network%20after%20each%20epoch.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>UltiHash for GANsGANs need large training datasets, often consisting of several hundreds of thousands of raw images. These images need to be stored&#8230;<img data-opt-id=1953450770  decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/faviconV2?url=https:\/\/www.ultihash.io&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">UltiHash<img data-opt-id=1628252069  decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/images?q=tbn:ANd9GcQXp3kx8Ov9ibxjxOspDmcbWop9vgBiODf0y2yg8U0cTgGGfZEe\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/arxiv.org\/html\/2502.08667v2#:~:text=Structural%20Similarity%20Index%20(SSIM)%20assesses%20the%20perceived,visual%20similarity%20than%20PSNR%20%5B%20131%5D%20.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Unpaired Image-to-Image Translation with Content Preserving Perspective: A ReviewFeb 14, 2025\u00a0\u2014\u00a0Structural Similarity Index (SSIM) assesses the perceived quality of images by comparing luminance, contrast, and stru&#8230;<img data-opt-id=1070120962  decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/arxiv.org&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">arXiv<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enhanced Generative Adversarial Networks for Unseen Word &#8230; &#8211; arXivNov 13, 2023 \u2014 Enhanced Generative Adversarial Networks for Unseen Word Generation from EEG Signals. &#8230; Recent advances in brain-com&#8230;<img data-opt-id=2035290517  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/95df5066-80b3-489b-aa8b-7bd46e5da001\" alt=\"\">arXiv<\/li>\n\n\n\n<li><a href=\"https:\/\/neurosciencenews.com\/ai-bci-movement-neurotech-29649\/#:~:text=Motor%20brain%E2%80%93computer%20interfaces%20(BCIs,autonomy%20may%20achieve%20higher%20performance.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Brain-AI System Translates Thoughts Into MovementAug 31, 2025 \u2014 Motor brain\u2013computer interfaces (BCIs) decode neural signals to help people with paralysis move and communicate. Even &#8230;<img data-opt-id=1081431231  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/7e500424-50a5-4296-a90b-cae9b1b12109\" alt=\"\">Neuroscience News<img data-opt-id=776448037  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/c758a172-3460-46f2-a61f-f6d173741675\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC8869956\/#:~:text=Abstract,future%20research%20directions%20are%20addressed.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>fMRI Brain Decoding and Its Applications in Brain\u2013Computer InterfaceAbstract. Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain\u2013comput&#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC10446822\/#:~:text=As%20the%20recorded%20neurons%20change,stabilizing%20iBCI%20systems%20over%20time.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Using adversarial networks to extend brain computer interface &#8230; &#8211; NIHAs the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If&#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC7327323\/#:~:text=Abstract,be%20headed%20in%20the%20future.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>The combination of brain-computer interfaces and artificial intelligenceAbstract. Brain-computer interfaces (BCIs) have shown great prospects as real-time bidirectional links between living brains and a&#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/vbn.aau.dk\/files\/462158798\/Generative_Adversarial_Networks_Based_Data_Augmentation_for_BrainComputer_Interface.pdf\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Generative Adversarial Networks-Based Data Augmentation for &#8230;In this diagram, XL denotes EEG samples from all subjects excluding target subject (LOO), Xt denotes target subject&#8217;s samples, Xh1&#8230;<img data-opt-id=870769310  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/ad425f00-446b-4736-9531-160ec484ec18\" alt=\"\">Aalborg Universitets forskningsportal<\/li>\n\n\n\n<li><a href=\"https:\/\/www.frontiersin.org\/journals\/neuroscience\/articles\/10.3389\/fnins.2025.1551656\/full#:~:text=For%20instance%2C%20in%20neurorehabilitation%2C%20SNNs,efficient%20and%20accurate%20BCI%20systems.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Multiscale fusion enhanced spiking neural network for invasive BCI &#8230;Feb 20, 2025 \u2014 For instance, in neurorehabilitation, SNNs can provide real-time feedback to assist in rehabilitation training (Elbasi&#8230;<img data-opt-id=601592479  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/e8b44fd8-0c7a-4bf2-891d-8a48810ef94b\" alt=\"\">Frontiers<img data-opt-id=775037692  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/818aafce-32b9-47fb-a23b-3f9648c616f6\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/cloud.google.com\/blog\/topics\/public-sector\/5-ai-trends-shaping-the-future-of-the-public-sector-in-2025#:~:text=Trend%20%231:%20Multimodal%20AI:,the%20power%20of%20multimodal%20AI.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>5 AI Trends Shaping the Future of Public Sector in 2025 &#8211; Google CloudFeb 5, 2025 \u2014 Trend #1: Multimodal AI: Unleashing the power of context Imagine a world where AI can understand and analyze informatio&#8230;<img data-opt-id=2132184658  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/11296816-1a3d-49df-bd49-52abb9babbfd\" alt=\"\">Google Cloud<img data-opt-id=2126409652  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/1c3702c5-312c-414c-a508-a8e01c25ca77\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/www.psychologytoday.com\/us\/blog\/the-future-brain\/202409\/brain-computer-interfaces-boosted-by-novel-ai-algorithm#:~:text=What%20sets%20the%20DPAD%20algorithm,step%20in%20their%20system%20architecture.&amp;text=The%20New%20Scholar:%20What's%20Next,in%20the%20Age%20of%20AI?&amp;text=%E2%80%9CThis%20AI%20algorithm%20can%20help,at%20the%20University%20of%20Pennsylvania.&amp;text=Cami%20Rosso%20writes%20about%20science%2C%20technology%2C%20innovation%2C%20and%20leadership.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Brain-Computer Interfaces Boosted by Novel AI AlgorithmSep 9, 2024 \u2014 What sets the DPAD algorithm apart from other recurring neural network algorithms used for decoding behavior from brain&#8230;<img data-opt-id=1109179263  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/5302ad6e-2ad0-4c37-a5a6-4fcd6ab548b9\" alt=\"\">Psychology Today<img data-opt-id=2019251680  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/ae8e6c31-9167-4286-9591-fc7a549fcb48\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/40786597\/#:~:text=Emerging%20Brain%2Dto%2DContent%20Technologies,AI%20and%20Deep%20Representation%20Learning\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Emerging Brain-to-Content Technologies from Generative AI and &#8230;Nov 14, 2024 \u2014 Emerging Brain-to-Content Technologies from Generative AI and Deep Representation Learning.<img data-opt-id=644833598  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/a9d578fe-7602-4d38-95eb-948e76fe0012\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/engineering.cmu.edu\/news-events\/news\/2024\/04\/30-noninvasive-bci.html#:~:text=Moreover%2C%20the%20capability%20of%20the,Jaehyun%20Lim%2C%20BME%20undergraduate%20student.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Refined AI approach improves noninvasive BCI performanceApr 29, 2024 \u2014 Moreover, the capability of the group&#8217;s AI-powered BCI suggests a direct application to continuously controlling a rob&#8230;<img data-opt-id=1146371293  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/c84e720d-34e8-4bcd-b76e-f339e03a461e\" alt=\"\">Carnegie Mellon University&#8217;s College of Engineering<img data-opt-id=2038533531  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/96c18b52-82ac-4690-b913-3a3ffa473972\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/milvus.io\/ai-quick-reference\/what-are-gans-and-how-do-they-help-in-data-augmentation#:~:text=GANs%20enhance%20data%20augmentation%20by,simple%20transformations%20to%20existing%20data.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>What are GANs, and how do they help in data augmentation? &#8211; MilvusWhat are GANs, and how do they help in data augmentation? * What are GANs? Generative Adversarial Networks (GANs) are a class of m&#8230;<img data-opt-id=1010052617  decoding=\"async\" src=\"blob:https:\/\/172-234-197-23.ip.linodeusercontent.com\/13cf7b91-fe2e-4a9c-9d38-6aaccd70c08b\" alt=\"\">Milvus<\/li>\n\n\n\n<li><a href=\"https:\/\/research.a-star.edu.sg\/articles\/highlights\/making-fake-brain-waves-more-realistic\/#:~:text=Despite%20the%20rapid%20progress%2C%20however%2C%20the%20performance,into%20a%20format%20that%20computers%20can%20use.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Making fake brain waves more realisticJul 19, 2021\u00a0\u2014\u00a0Despite the rapid progress, however, the performance of BCI ( Brain\u2013computer interfaces ) technology continues to be l&#8230;<img data-opt-id=1953450770  decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/faviconV2?url=https:\/\/research.a-star.edu.sg&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">Agency for Science, Technology and Research<img data-opt-id=1649756752  decoding=\"async\" src=\"https:\/\/encrypted-tbn2.gstatic.com\/images?q=tbn:ANd9GcRD0XzImslaM-nhN5L7vSso2e42jfwbxqOZR9TNn8H_W9R-jJiK\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/www.frontiersin.org\/journals\/human-neuroscience\/articles\/10.3389\/fnhum.2023.1111645\/full#:~:text=In%20brain%2Dcomputer%20interfaces%20(BCI)%20research%2C%20recording%20data,expensive%2C%20which%20limits%20access%20to%20big%20datasets.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Impact of dataset size and long-term ECoG-based BCI usage on deep learning decoders performanceMar 15, 2023\u00a0\u2014\u00a0In brain-computer interfaces (BCI) research, recording data is time-consuming and expensive, which limits access to bi&#8230;<img data-opt-id=25266728  decoding=\"async\" src=\"https:\/\/encrypted-tbn2.gstatic.com\/faviconV2?url=https:\/\/www.frontiersin.org&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">Frontiers<img data-opt-id=1649756752  decoding=\"async\" src=\"https:\/\/encrypted-tbn2.gstatic.com\/images?q=tbn:ANd9GcQE4kgZyzoBuUTVb_D3XTeGUgt_GdsWvnO2VHU_k8aIwlU1mxq7\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/www.researchgate.net\/publication\/286951613_Brain-Computer_Interfaces_Something_New_under_the_Sun#:~:text=EEG%20has%20been%20used%20in%20the%20majority,develop%20a%20brain%2Dcomputer%20interface%20(BCI)%20%5B51%5D%20.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Brain-Computer Interfaces: Something New under the Sun | Request PDF&#8230; EEG has been used in the majority of mental workload research based on brain activity, which comes after several studies that &#8230;<img data-opt-id=25266728  decoding=\"async\" src=\"https:\/\/encrypted-tbn2.gstatic.com\/faviconV2?url=https:\/\/www.researchgate.net&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">ResearchGate<\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC12386253\/#:~:text=Introduction:%20The%20advancement%20of%20artificial%20intelligence%20(AI),high%20expertise%20required%20for%20labeling%20neurosurgical%20data.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Publicly Available Datasets for Artificial Intelligence in Neurosurgery: A Systematic ReviewAug 10, 2025\u00a0\u2014\u00a0Introduction: The advancement of artificial intelligence (AI) in neurosurgery is dependent on high quality, large, lab&#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/38943984\/#:~:text=Recent%20advancements%20in%20brain%2Dcomputer%20interface%20(BCI)%20technology,such%20as%20regression%20for%20decoding%20arbitrary%20movements.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Dual model transfer learning to compensate for individual variability in brain-computer interfaceSep 14, 2024\u00a0\u2014\u00a0Recent advancements in brain-computer interface (BCI) technology have seen a significant shift towards incorporating c&#8230;<img data-opt-id=639115315  decoding=\"async\" src=\"https:\/\/encrypted-tbn3.gstatic.com\/faviconV2?url=https:\/\/pubmed.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/www.mdpi.com\/2076-3425\/14\/3\/234#:~:text=Abstract.%20Reconstructing%20natural%20stimulus%20images%20using%20functional,the%20information%20about%20interactions%20among%20brain%20regions.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Natural Image Reconstruction from fMRI Based on Node\u2013Edge &#8230;Feb 27, 2024\u00a0\u2014\u00a0Abstract. Reconstructing natural stimulus images using functional magnetic resonance imaging (fMRI) is one of the most&#8230;<img data-opt-id=1070120962  decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/www.mdpi.com&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">MDPI<img data-opt-id=1649756752  decoding=\"async\" src=\"https:\/\/encrypted-tbn2.gstatic.com\/images?q=tbn:ANd9GcTfWvvqaIS8TPYH3Nj63mTy9ORf81WNiNJOVJpOQQOtllFLbLOo\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/mlearning.substack.com\/p\/ai-brain-activity-art-mind-video-reconstruction#:~:text=All%20five%20studies%20focus%20on%20reconstructing%20images,(2023)%20and%20contrastive%20learning%2C%20to%20achieve%20this.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>From Brain Activity to Videos: The Journey of Mind VideoMay 21, 2023\u00a0\u2014\u00a0All five studies focus on reconstructing images or videos from brain activity, specifically using fMRI data. They all &#8230;<img data-opt-id=639115315  decoding=\"async\" src=\"https:\/\/encrypted-tbn3.gstatic.com\/faviconV2?url=https:\/\/mlearning.substack.com&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">MLearning.ai Art<img data-opt-id=1628252069  decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/images?q=tbn:ANd9GcTRltxP_3cJAx5Yoxs8jZHoxdBtfKmM1bZPjV0EQAhILTtQEgjy\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/www.hilarispublisher.com\/open-access\/the-intersection-of-ai-and-neuroengineering-transforming-brainmachine-interfaces-109724.html#:~:text=Traditional%20BMIs%20often%20required%20manual%20calibration%20and,adapt%20to%20changes%20in%20neural%20activity%20patterns.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>The Intersection of AI and Neuroengineering: Transforming Brain-machine InterfacesTraditional BMIs often required manual calibration and adjustment to maintain accuracy, a process that could be cumbersome and tim&#8230;<img data-opt-id=25266728  decoding=\"async\" src=\"https:\/\/encrypted-tbn2.gstatic.com\/faviconV2?url=https:\/\/www.hilarispublisher.com&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">Hilaris Publishing SRL<img data-opt-id=1219115883  decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/images?q=tbn:ANd9GcRBFYfTCWdqckocD8y0QWf7PDgscdrNtyRbkf7dGJkpbboTZ0p_\" alt=\"\"><\/li>\n\n\n\n<li><a href=\"https:\/\/www.biorxiv.org\/content\/10.1101\/2025.04.20.649482.full#:~:text=Practical%20application%20of%20brain%2Dcomputer%20interfaces%20(BCIs)%20requires,require%20frequent%20recalibration%20to%20maintain%20robust%20performance.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Meta-AlignNN: A meta-learning framework for stable brain-computer interface performance across subjects, time, and tasksApr 24, 2025\u00a0\u2014\u00a0Practical application of brain-computer interfaces (BCIs) requires stable mapping between neuronal activity and behavi&#8230;<img data-opt-id=25266728  decoding=\"async\" src=\"https:\/\/encrypted-tbn2.gstatic.com\/faviconV2?url=https:\/\/www.biorxiv.org&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">bioRxiv<\/li>\n\n\n\n<li><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC11245071\/#:~:text=Deep%2Dlearning%20(DL)%20based%20generative%20models%20such%20as,%5B%2013%5D%2C%20and%20biomedicine%5B%2014%5D%2C%20%5B%2015%5D.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Diffusion model enables quantitative CBF analysis of Alzheimer\u2019s DiseaseJul 2, 2024\u00a0\u2014\u00a0Deep-learning (DL) based generative models such as generative adversarial networks (GANs) and variational auto-encoders&#8230;<img data-opt-id=1070120962  fetchpriority=\"high\" decoding=\"async\" src=\"https:\/\/encrypted-tbn1.gstatic.com\/faviconV2?url=https:\/\/pmc.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n\n\n\n<li><a href=\"https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK326719\/#:~:text=However%2C%20chronic%20brain%2Dmachine%20interfaces%20show%20that%20the,functional%20changes%20as%20well%20as%20network%20alterations.\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>Enhanced Functional Outcome from Traumatic Brain Injury with Brain\u2013Machine Interface NeuromodulationApr 27, 2023\u00a0\u2014\u00a0However, chronic brain-machine interfaces show that the underlying network can be modified over time, particularly in &#8230;<img data-opt-id=1953450770  decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/faviconV2?url=https:\/\/www.ncbi.nlm.nih.gov&amp;client=AIM&amp;size=128&amp;type=FAVICON&amp;fallback_opts=TYPE,SIZE,URL\" alt=\"\">National Institutes of Health (.gov)<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Training and evaluating&nbsp;GANs&nbsp;for brain activity-based image reconstruction is typically a multi-step process involving&nbsp;pre-training on large image datasets, mapping fMRI data to a latent space, and using a combination of quantitative metrics and qualitative human assessment.&nbsp; Training Process The training process usually involves a hybrid framework and can be broken down into three main stages:&nbsp; Evaluation&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4655\" rel=\"bookmark\"><span class=\"screen-reader-text\">Cycle-Consistent Adversarial Networks<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[6,7],"tags":[],"class_list":["post-4655","post","type-post","status-publish","format-standard","hentry","category-signal-science","category-the-truben-show"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4655","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4655"}],"version-history":[{"count":2,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4655\/revisions"}],"predecessor-version":[{"id":4658,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/4655\/revisions\/4658"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4655"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4655"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4655"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}