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Bridging Multi-Imaging Modalities Using Deep Learning for Comprehensive Insights on Resting State Brain Activities

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  • 발행기관
    선문효정학술연구회 바로가기
  • 간행물
    선문효정학술연구회 학술대회 프로시딩 바로가기
  • 통권
    Proceedings of THE 4th INTERNATIONAL CONFERENCE OF HYOJEONG ACADEMY & 2024 INTERNATIONAL JOINT CONFERENCE (2024.08)바로가기
  • 페이지
    pp.90-95
  • 저자
    Bruno Grace, Gyuseok Lee, Jörg Stadler, André Brechmann, Wonsang You
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A489296

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원문정보

초록

영어
In the pursuit of understanding the complexities of the human brain, combining Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) provides a powerful approach. This paper introduces a methodology to merge these two brain imaging techniques, aiming to leverage their complementary strengths. EEG captures the rapid dynamics of neural activity with high temporal resolution, while fMRI offers detailed localization of brain functions with superior spatial resolution. By integrating EEG and fMRI, we can overcome the limitations of each technique when used separately, resulting in a more comprehensive understanding of brain activity. We discuss advanced techniques for synchronizing and integrating EEG and fMRI data which are acquired simultaneously in resting state, highlighting the role of innovative computational methods and machine learning algorithms in enhancing data fusion. We have developed a Transformer-CNN model to generate fMRI data from EEG and have assessed the accuracy of the predicted fMRI data compared to the ground truth both quantitatively and qualitatively. Our study is still ongoing, but it demonstrates the potential of leveraging deep learning for enhancing multimodal brain imaging. We have also shown the feasibility and applicability of synthesizing EEGto- fMRI in resting state, which could be valuable for neuroscientific research.

목차

Abstract
1. Introduction
2. Materials and Methods
2.1. Dataset
2.2. Data Preprocessing
2.3. Transformer-CNN model
3. Results
4. Discussion
References

키워드

Multimodal imaging deep learning EEG fMRI

저자

  • Bruno Grace [ AIIP Lab, Department of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ]
  • Gyuseok Lee [ AIIP Lab, Department of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ]
  • Jörg Stadler [ Combinatorial Neuroimaging Core Facility, Leibniz Institute for Neurobiology, 39118 Magbeburg, Germany ]
  • André Brechmann [ Combinatorial Neuroimaging Core Facility, Leibniz Institute for Neurobiology, 39118 Magbeburg, Germany ]
  • Wonsang You [ AIIP Lab, Department of Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    선문효정학술연구회 [Sun Moon Hyojeong Academy Society]
  • 설립연도
    2023
  • 분야
    복합학>학제간연구
  • 소개
    Journal of Hyojeong Academia aims to serve as a global platform where researchers and scholars of various disciplines can contribute ideas for our sustainable global community of Co‐existence, Co‐prosperity, and Co‐righteousness. The journal is a multidisciplinary, open‐access, internationally peer‐reviewed academic journal, and it invites all areas of research conducted in the spirit of post materialism including studies centering on God, studies unifying religions and sciences, and studies on all aspects of Co‐existence, Co‐prosperity, and Co‐righteousness.

간행물

  • 간행물명
    선문효정학술연구회 학술대회 프로시딩
  • 간기
    반년간
  • 수록기간
    2023~2026
  • 십진분류
    KDC 238 DDC 289

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