DCAE: A dual conditional autoencoder framework for the reconstruction from EEG into image

Zeng, Hong; Xia, Nianzhang; Tao, Ming; Deng, Pan; Zheng, Haohao; Wang, Chu; Xu, Feifan; Zakaria, Wael; Dai, Guojun

doi:10.1016/j.bspc.2022.104440

Cited by 10 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We hope that everyone will learn how to conduct these analyses and understand why they should be done by exploring more literature. Moreover, our handbook still lacks coverage of many topics that may be of interest, such as brain connectivity analysis based on resting‐state EEG, [22–24] image reconstruction from EEG data, [25–29] and research combining artificial neural networks with EEG data [16,17,30–32] . We will add more content to the handbook to make this tutorial more comprehensive.…”

Section: Discussionmentioning

confidence: 99%

An easy‐to‐follow handbook for electroencephalogram data analysis with Python

Lu,

Li,

Nie

et al. 2024

Brain-X

View full text Add to dashboard Cite

This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive neuroscience and related fields. It spans from single‐subject data preprocessing to advanced multisubject analyses. This handbook contains four chapters: Preprocessing Single‐Subject Data, Basic Python Data Operations, Multiple‐Subject Analysis, and Advanced EEG Analysis. The Preprocessing Single‐Subject Data chapter provides a standardized procedure for single‐subject EEG data preprocessing, primarily using the MNE‐Python package. The Basic Python Data Operations chapter introduces essential Python operations for EEG data handling, including data reading, storage, and statistical analysis. The Multiple‐Subject Analysis chapter guides readers on performing event‐related potential and time‐frequency analyses and visualizing outcomes through examples from a face perception task dataset. The Advanced EEG Analysis chapter explores three advanced analysis methodologies, Classification‐based decoding, Representational Similarity Analysis, and Inverted Encoding Model, through practical examples from a visual working memory task dataset using NeuroRA and other powerful packages. We designed our handbook for easy comprehension to be an essential tool for anyone delving into EEG data analysis with Python (GitHub website: https://github.com/ZitongLu1996/Python‐EEG‐Handbook; For Chinese version: https://github.com/ZitongLu1996/Python‐EEG‐Handbook‐CN).

show abstract

Section: Discussionmentioning

confidence: 99%

An easy‐to‐follow handbook for electroencephalogram data analysis with Python

Lu,

Li,

Nie

et al. 2024

Brain-X

View full text Add to dashboard Cite

show abstract

“…In addition, the advancements in AI have continuously promoted the results of brain encoding and decoding, interpreting brain signals into text, vocal language, and images. For instance, the acoustic interpretation of EEG brain signals, converting EEG to sound using an AI-driven attention mechanism (Gomez-Quintana et al, 2022); the interpretation of human thoughts, captured via fMRI, into words by using GPT (Tang et al, 2023); and the reconstruction from EEG signals to corresponding images based on diffusion model (Zeng et al, 2023). Bringing together AI and cognitive science has thereby offered methods and approaches to study design from new perspectives.…”

Section: Future Research Directionsmentioning

confidence: 99%

Applications of artificial intelligence and cognitive science in design

Han,

Childs,

Luo

2024

AIEDAM

View full text Add to dashboard Cite

Artificial intelligence and cognitive science are two core research areas in design. Artificial intelligence shows the capability of analysing massive amounts of data which supports making predictions, uncovering patterns and generating insights in varying design activities, while cognitive science provides the advantage of revealing the inherent mental processes and mechanisms of humans in design. Both artificial intelligence and cognitive science in design research are focused on delivering more innovative and efficient design outcomes and processes. Therefore, this thematic collection on “Applications of Artificial Intelligence and Cognitive Science in Design” brings together state-of-the-art research in artificial intelligence and cognitive science to showcase the emerging trend of applying artificial intelligence techniques and neurophysiological and biometric measures in design research. Three promising future research directions: 1) human-in-the-loop AI for design, 2) multimodal measures for design, and 3) AI for design cognitive data analysis and interpretation, are suggested by analysing the research papers collected. A framework for integration of artificial intelligence and cognitive science in design, incorporating the three research directions, is proposed to inspire and guide design researchers in exploring human-centred design methods, strategies, solutions, tools and systems.

show abstract

“…Examples include visual-semantic models (Radford et al ., 2021 ), and high-performance diffusion models (Rombach et al ., 2022 ). With state-of-the-art models and rich neuroimaging data resources, a number of recent studies are able to reconstruct or generate remarkably high fidelity images from brain activity measured by fMRI (Gu et al ., 2023 ; Lin et al ., 2022 ; Ozcelik & VanRullen, 2023 ; Scotti et al ., 2023 ; Takagi & Nishimoto, 2023 ) and EEG (Lan et al ., 2023 ; Singh et al ., 2023 ; Wakita et al ., 2021 ; Zeng et al ., 2023 ). However, we need to interpret these recent reconstruction results with caution.…”

Section: Rapid Developments Of Image Reconstruction Techniques In Cog...mentioning

confidence: 99%

Visualizing the mind's eye: a future perspective on applications of image reconstruction from brain signals to psychiatry

2023

Psychoradiology

View full text Add to dashboard Cite

In an era where neuroscience dances with computational advances, the power to “visualize” one's thoughts at image-level is no longer confined to the realm of science fiction. This groundbreaking image reconstruction from brain signals (IRBS) techniques, riding the wave of deep learning and large-scale neuroimaging datasets, offer an unprecedented perspective for not only neurocognitive research but also psychiatry. In this short commentary, I provide a concise introduction to the development of IRBS technology and offer a perspective on its potential applications, current limitations, and future directions in psychiatry.

show abstract

DCAE: A dual conditional autoencoder framework for the reconstruction from EEG into image

Cited by 10 publications

References 32 publications

An easy‐to‐follow handbook for electroencephalogram data analysis with Python

An easy‐to‐follow handbook for electroencephalogram data analysis with Python

Applications of artificial intelligence and cognitive science in design

Visualizing the mind's eye: a future perspective on applications of image reconstruction from brain signals to psychiatry

Contact Info

Product

Resources

About