While functional magnetic resonance imaging (fMRI) offers valuable insights into brain activity, it is limited by high operational costs and significant infrastructural demands. In contrast, electroencephalography (EEG) provides millisecond-level precision in capturing electrical activity but lacks the spatial fidelity necessary for precise neural localization. To bridge these gaps, we propose E2fNet, a simple yet effective deep learning model for synthesizing fMRI images from low-cost EEG data. E2fNet is an encoder-decoder network specifically designed to capture and translate meaningful multi-scale features from EEG across electrode channels into accurate fMRI representations. Extensive evaluations across three public datasets demonstrate that E2fNet consistently outperforms existing CNN- and transformer-based methods, achieving state-of-the-art results in terms of the structural similarity index measure (SSIM). These results demonstrate that E2fNet is a promising, cost-effective solution for enhancing neuroimaging capabilities. The code is available at https://github.com/kgr20/E2fNet.
Brainwave signals are read through Electroencephalogram (EEG) devices. These signals are generated from an active brain based on brain activities and thoughts. The classification of brainwave signals is a challenging task due to its non-stationary nature. To address the issue, this paper proposes a Convolutional Neural Network (CNN) model to classify brainwave signals. In order to evaluate the performance of the proposed model a dataset is developed by recording brainwave signals for two conditions, which are visible and invisible. In the visible mode, the human subjects focus on the color and…
Univ Rennes（雷恩大学）；INRIA（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；University Hospital of Rennes（雷恩大学医院）
Simultaneous EEG/fMRI acquisition allows to measure brain activity at high spatial-temporal resolution. The localisation of EEG sources depends on several parameters including the position of the electrodes on the scalp. The position of the MR electrodes during its acquisitions is obtained with the use of the UTE sequence allowing their visualisation. The retrieval of the electrodes consists in obtaining the volume where the electrodes are located by applying a sphere detection algorithm. We detect around 90% of electrodes for each subject, and our UTE-based electrode detection showed an avera…
Simultaneous EEG-fMRI is a multi-modal neuroimaging technique that provides complementary spatial and temporal resolution. Challenging has been developing principled and interpretable approaches for fusing the modalities, specifically approaches enabling inference of latent source spaces representative of neural activity. In this paper, we address this inference problem within the framework of transcoding -- mapping from a specific encoding (modality) to a decoding (the latent source space) and then encoding the latent source space to the other modality. Specifically, we develop a symmetric me…
Spec2VolCAMU-Net: A Spectrogram-to-Volume Model for EEG-to-fMRI Reconstruction based on Multi-directional Time-Frequency Convolutional Attention Encoder and Vision-Mamba U-Net
School of Artificial Intelligence, Chongqing University of Technology（人工智能学院，重庆理工大学）
Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning
Duke University（杜克大学）；Harvard University（哈佛大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Massachusetts General Hospital（麻省总医院）；Johns Hopkins Medicine（约翰霍普金斯医学院）；Emory University（埃默里大学）；Yale University（耶鲁大学）；University of Nebraska Medical Center（内布拉斯加大学医学中心）；University of Mississippi Medical Center（密西西比大学医学中心）；Mayo Clinic（梅奥诊所）
CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation
Southern University of Science and Technology（南方科技大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Uppsala University（乌普萨拉大学）；King Fahd University of Petroleum and Minerals（法赫德国王石油与矿业大学）；Maebashi Institute of Technology（前桥工业大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）；Shenzhen University（深圳大学）
Lehigh University（利哈伊大学）；Cumberland Valley High School（坎伯兰谷高中）；Beihang University（北京航空航天大学）；Emory University（埃默里大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
This paper presents a novel graph-based kernel learning approach for connectome analysis. Specifically, we demonstrate how to leverage the naturally available structure within the graph representation to encode prior knowledge in the kernel. We first proposed a matrix factorization to directly extract structural features from natural symmetric graph representations of connectome data. We then used them to derive a structure-persevering graph kernel to be fed into the support vector machine. The proposed approach has the advantage of being clinically interpretable. Quantitative evaluations on c…
Universiti Teknologi Malaysia（马来西亚理工大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure. We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary aspects of disrupted connectivity in SZ, we explore combination of vario…
