Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Guangdong University of Technology（广东工业大学）
Shuqiang Wang, Tong Zhou, Yanyan Shen, Ye Li, Guoheng Huang, Yong Hu
Electroencephalogram (EEG) technology, particularly high-density EEG (HD EEG) devices, is widely used in fields such as neuroscience. HD EEG devices improve the spatial resolution of EEG by placing more electrodes on the scalp, which meet the requirements of clinical diagnostic applications such as epilepsy focus localization. However, this technique faces challenges, such as high acquisition costs and limited usage scenarios. In this paper, spatio-temporal adaptive diffusion models (STAD) are proposed to pioneer the use of diffusion models for achieving spatial SR reconstruction from low-resolution (LR, 64 channels or fewer) EEG to high-resolution (HR, 256 channels) EEG. Specifically, a spatio-temporal condition module is designed to extract the spatio-temporal features of LR EEG, which are then used as conditional inputs to direct the reverse denoising process. Additionally, a multi-scale Transformer denoising module is constructed to leverage multi-scale convolution blocks and cross-attention-based diffusion Transformer blocks for conditional guidance to generate subject-adaptive SR EEG. Experimental results demonstrate that the STAD significantly enhances the spatial resolution of LR EEG and quantitatively outperforms existing methods. Furthermore, STAD demonstrate their value by applying synthetic SR EEG to classification and source localization tasks, indicating their potential to substantially boost the spatial resolution of EEG.
Advances on signal, image and video generation underly major breakthroughs on generative medical imaging tasks, including Brain Image Synthesis. Still, the extent to which functional Magnetic Ressonance Imaging (fMRI) can be mapped from the brain electrophysiology remains largely unexplored. This work provides the first comprehensive view on how to use state-of-the-art principles from Neural Processing to synthesize fMRI data from electroencephalographic (EEG) data. Given the distinct spatiotemporal nature of haemodynamic and electrophysiological signals, this problem is formulated as the task…
Geometry- and Relation-Aware Diffusion for EEG Super-Resolution
Electroencephalography (EEG) provides a noninvasive window into distributed cortical dynamics of brain and is widely used across affective computing（脑电图（EEG）为研究大脑分布式皮层动态提供了一种非侵入性窗口，并广泛应用于情感计算）
University of Sheffield（谢菲尔德大学）；University of Oxford（牛津大学）
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian process describes such structure and the interdependencies are represented via the covariance matrices of the prior distributions. The inference is based on the expectation propagation method and the theoretical derivation of the posterior distribution is provided in the paper. Th…
MSGM: A Multi-Scale Spatiotemporal Graph Mamba for EEG Emotion Recognition
School of Electronics and Communication Engineering, Sun Yat-sen University（中山大学电子与通信工程学院）；College of Communication Engineering, Jilin University（吉林大学通信工程学院）；School of Pharmaceutical Sciences (Shenzhen), Sun Yat-sen University（中山大学药学院（深圳））；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
University of Mons（蒙斯大学）；IMT Nord Europe（北欧国立高等矿业电信学院）；CNRS（法国国家科学研究中心）
For many years now, understanding the brain mechanism has been a great research subject in many different fields. Brain signal processing and especially electroencephalogram (EEG) has recently known a growing interest both in academia and industry. One of the main examples is the increasing number of Brain-Computer Interfaces (BCI) aiming to link brains and computers. In this paper, we present a novel framework allowing us to retrieve the attention state, i.e degree of attention given to a specific task, from EEG signals. While previous methods often consider the spatial relationship in EEG th…
Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；DSO National Laboratories（新加坡国防科技局实验室）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光前医学院）；National University Health System（国立大学卫生系统）；Division of Psychology, Nanyang Technological University（南洋理工大学心理学系）；Department of Pediatrics, University of Cambridge（剑桥大学儿科系）；Yong Loo Lin School of Medicine, National University of Singapore（新加坡国立大学杨潞龄医学院）；Department of Clinical Neuroscience, Karolinska Institutet（卡罗林斯卡学院临床神经科学系）
Unlike conventional data such as natural images, audio and speech, raw multi-channel Electroencephalogram (EEG) data are difficult to interpret. Modern deep neural networks have shown promising results in EEG studies, however finding robust invariant representations of EEG data across subjects remains a challenge, due to differences in brain folding structures. Thus, invariant representations of EEG data would be desirable to improve our understanding of the brain activity and to use them effectively during transfer learning. In this paper, we propose an approach to learn deep representations …
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals
École de technologie supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Dibrugarh University（Dibrugarh大学）；Vytautas Magnus University（维陶塔斯马格努斯大学）；University of Southern Queensland（南昆士兰大学）
Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual feature engineering and domain knowledge. In this study, we propose an adaptive scheme to probabilistically encode, filter and accumulate the input signals and weight the resultant features by the half-Gaussian probabilities of signal intensities. The adaptive representations are subsequently fed into a transformer model to automatically mine the relevance between features and corresponding stages. Extensive experiment…
miMamba: EEG-based Emotion Recognition with Multi-scale Inverted Mamba Models
Department of Computer Science, State University of New York at Binghamton（纽约州立大学布法罗分校计算机科学系）；College of Big Data and Internet, Shenzhen Technology University（深圳科技大学大数据与互联网学院）
KU Leuven（鲁汶大学）；University of Piraeus（比雷埃夫斯大学）；KU Leuven Kulak（鲁汶大学科特赖克校区）；Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
Data fusion refers to the joint analysis of multiple datasets which provide complementary views of the same task. In this preprint, the problem of jointly analyzing electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) data is considered. Jointly analyzing EEG and fMRI measurements is highly beneficial for studying brain function because these modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The fusion methods reported so far ignore the underlying multi-way nature of the data in …
There is abundant medical data on the internet, most of which are unlabeled. Traditional supervised learning algorithms are often limited by the amount of labeled data, especially in the medical domain, where labeling is costly in terms of human processing and specialized experts needed to label them. They are also prone to human error and biased as a select few expert annotators label them. These issues are mitigated by Self-supervision, where we generate pseudo-labels from unlabelled data by seeing the data itself. This paper presents various self-supervision strategies to enhance the perfor…
Electrophysiological signals (electroencephalography, EEG, and magnetoencephalography , MEG), as many natural processes, exhibit scale-invariance properties resulting in a power-law (1/f) spectrum. Interestingly, EEG and MEG differ in their slopes, which could be explained by several mechanisms, including non-resistive properties of tissues. Our goal in the present study is to estimate the impact of space/frequency structure of source signals as a putative mechanism to explain spectral scaling properties of neuroimaging signals. We performed simulations based on the summed contribution of cort…
Technische Universität Berlin（柏林工业大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Central China Normal University（华中师范大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Physikalisch-Technische Bundesanstalt（德国联邦物理技术研究院）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Bernstein Center for Computational Neuroscience（伯恩斯坦计算神经科学中心）
Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models. Examples include M/EEG inverse problems, neural encoding models for task-based fMRI analyses, and climate science. In these domains, both the model parameters to be inferred and the measurement noise may exhibit a complex spatio-temporal structure. Existing work either neglects the temporal structure or leads to computationally demanding inference schemes. Overcoming these limitations, we devise a novel flexible hierarchical Bayesian framework within which the sp…
Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition. To solve this, a factorization appro…
Coordinate-Queryable Neural Field Reconstruction for EEG Spatial Super-Resolution with Unseen-Electrode Generation
School of Intelligence Science and Technology, University of Science and Technology Beijing（信息科学与技术学院，北京科技大学）；School of Computer and Communication Engineering, University of Science and Technology Beijing（计算机与通信工程学院，北京科技大学）
TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolution
Lab of Digital Image and Intelligent Computation, Shanghai Maritime University（数字图像与智能计算实验室，上海海洋大学）；Department of Language Science and Technology, The Hong Kong Polytechnic University（语言科学与技术系，香港理工大学）；Affiliated Lianyungang Hospital of Xuzhou Medical University（徐州医学院连云港医院）
