Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models

• URL: http://arxiv.org/abs/2410.02780v1
• Date: Tue, 17 Sep 2024 19:07:13 GMT
• Title: Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models
• Authors: Eleonora Lopez, Luigi Sigillo, Federica Colonnese, Massimo Panella, Danilo Comminiello,
• Abstract summary: EEG is a low-cost, non-invasive, and portable neuroimaging technique. EEG presents inherent challenges due to its low spatial resolution and susceptibility to noise and artifacts. We propose a framework based on the ControlNet adapter for conditioning a latent diffusion model through EEG signals.
• Score: 4.933734706786783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has focused on fMRI-to-Image tasks as fMRI is characterized by high spatial resolution. However, fMRI is an expensive neuroimaging modality and does not allow for real-time BCI. On the other hand, electroencephalography (EEG) is a low-cost, non-invasive, and portable neuroimaging technique, making it an attractive option for future real-time applications. Nevertheless, EEG presents inherent challenges due to its low spatial resolution and susceptibility to noise and artifacts, which makes generating images from EEG more difficult. In this paper, we address these problems with a streamlined framework based on the ControlNet adapter for conditioning a latent diffusion model (LDM) through EEG signals. We conduct experiments and ablation studies on popular benchmarks to demonstrate that the proposed method beats other state-of-the-art models. Unlike these methods, which often require extensive preprocessing, pretraining, different losses, and captioning models, our approach is efficient and straightforward, requiring only minimal preprocessing and a few components. Code will be available after publication.

Related papers

• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning [2.087148326341881]
  This paper introduces a MUltimodal Similarity-keeping contrastivE learning framework for zero-shot EEG-based image classification. We develop a series of multivariate time-series encoders tailored for EEG signals and assess the efficacy of regularized contrastive EEG-Image pretraining. Our method achieves state-of-the-art performance, with a top-1 accuracy of 19.3% and a top-5 accuracy of 48.8% in 200-way zero-shot image classification.
  arXiv  Detail & Related papers  (2024-06-05T16:42:23Z)
• MindShot: Brain Decoding Framework Using Only One Image [21.53687547774089]
  MindShot is proposed to achieve effective few-shot brain decoding by leveraging cross-subject prior knowledge. New subjects and pretrained individuals only need to view images of the same semantic class, significantly expanding the model's applicability.
  arXiv  Detail & Related papers  (2024-05-24T07:07:06Z)
• NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation [55.51412454263856]
  This paper proposes to directly modulate the generation process of diffusion models using fMRI signals. By training with about 67,000 fMRI-image pairs from various individuals, our model enjoys superior fMRI-to-image decoding capacity.
  arXiv  Detail & Related papers  (2024-03-27T02:42:52Z)
• Leveraging sinusoidal representation networks to predict fMRI signals from EEG [3.3121941932506473]
  We propose a novel architecture that can predict fMRI signals directly from multi-channel EEG without explicit feature engineering. Our model achieves this by implementing a Sinusoidal Representation Network (SIREN) to learn frequency information in brain dynamics. We evaluate our model using a simultaneous EEG-fMRI dataset with 8 subjects and investigate its potential for predicting subcortical fMRI signals.
  arXiv  Detail & Related papers  (2023-11-06T03:16:18Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• DreamDiffusion: Generating High-Quality Images from Brain EEG Signals [42.30835251506628]
  DreamDiffusion is a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals. The proposed method overcomes the challenges of using EEG signals for image generation, such as noise, limited information, and individual differences.
  arXiv  Detail & Related papers  (2023-06-29T13:33:02Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• fMRI from EEG is only Deep Learning away: the use of interpretable DL to unravel EEG-fMRI relationships [68.8204255655161]
  We present an interpretable domain grounded solution to recover the activity of several subcortical regions from multichannel EEG data. We recover individual spatial and time-frequency patterns of scalp EEG predictive of the hemodynamic signal in the subcortical nuclei.
  arXiv  Detail & Related papers  (2022-10-23T15:11:37Z)
• EEG to fMRI Synthesis: Is Deep Learning a candidate? [0.913755431537592]
  This work provides the first comprehensive on how to use state-of-the-art principles from Neural Processing to synthesize fMRI data from electroencephalographic (EEG) view data. A comparison of state-of-the-art synthesis approaches, including Autoencoders, Generative Adrial Networks and Pairwise Learning, is undertaken. Results highlight the feasibility of EEG to fMRI brain image mappings, pinpointing the role of current advances in Machine Learning and showing the relevance of upcoming contributions to further improve performance.
  arXiv  Detail & Related papers  (2020-09-29T16:29:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.