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

TL;DR

This paper presents a streamlined EEG-to-image generation framework using latent diffusion models conditioned with ControlNet, achieving superior results with minimal preprocessing compared to existing complex methods.

Contribution

The authors introduce a simple, efficient EEG-to-image generation method leveraging ControlNet and latent diffusion models, reducing preprocessing and complexity.

Findings

Outperforms state-of-the-art models on benchmark datasets

Requires minimal preprocessing and components

Demonstrates effective EEG-to-image translation

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. The code is available at https://github.com/LuigiSigillo/GWIT.