Those experiencing strokes, traumatic brain injuries, and drug complications
can often end up hospitalized and diagnosed with coma or locked-in syndrome.
Such mental impediments can permanently alter the neurological pathways in work
and significantly decrease the quality of life (QoL). It is critical to
translate brain signals into images to gain a deeper understanding of the
thoughts of a comatose patient. Traditionally, brain signals collected by an
EEG could only be translated into text, but with the novel method of an
open-source model available on GitHub, DreamDiffusion can be used to convert
brain waves into images directly. DreamDiffusion works by extracting features
from EEG signals and then using the features to create images through
StableDiffusion. Upon this, we made further improvements that could make
StableDiffusion the forerunner technology in waves to media translation. In our
study, we begin by modifying the existing DreamDiffusion codebase so that it
does not require any prior setup, avoiding any confusing steps needed to run
the model from GitHub. For many researchers, the incomplete setup process,
errors in the existing code, and a lack of directions made it nearly impossible
to run, not even considering the model's performance. We brought the code into
Google Colab so users could run and evaluate problems cell-by-cell, eliminating
the specific file and repository dependencies. We also provided the original
training data file so users do not need to purchase the necessary computing
power to train the model from the given dataset. The second change is utilizing
the mutability of the code and optimizing the model so it can be used to
generate images from other given inputs, such as sleep data. Additionally, the
affordability of EEG technology allows for global dissemination and creates the
opportunity for those who want to work on the shared DreamDiffusion model.

经过改进的 DreamDiffusion 模型使用 EEG 信号提取特征，通过 StableDiffusion 将信号转化为图像，从而帮助理解昏迷患者的思维。

利用 DreamDiffusion 的图像脑电图睡眠数据的新方法来改善脑损伤患者的生活质量

A Novel Approach to Image EEG Sleep Data for Improving Quality of Life  in Patients Suffering From Brain Injuries Using DreamDiffusion

This paper introduces DreamDiffusion, a novel method for generating
high-quality images directly from brain electroencephalogram (EEG) signals,
without the need to translate thoughts into text. DreamDiffusion leverages
pre-trained text-to-image models and employs temporal masked signal modeling to
pre-train the EEG encoder for effective and robust EEG representations.
Additionally, the method further leverages the CLIP image encoder to provide
extra supervision to better align EEG, text, and image embeddings with limited
EEG-image pairs. Overall, the proposed method overcomes the challenges of using
EEG signals for image generation, such as noise, limited information, and
individual differences, and achieves promising results. Quantitative and
qualitative results demonstrate the effectiveness of the proposed method as a
significant step towards portable and low-cost ``thoughts-to-image'', with
potential applications in neuroscience and computer vision.

DreamDiffusion 提出了一种新的方式，可以直接从大脑的脑电信号中生成高质量的图像，同时克服了诸多挑战，并在神经科学和计算机视觉等领域有潜在应用。