Neuroimaging techniques have shown to be useful when studying the brain's activity. This paper uses Magnetoencephalography (MEG) data, provided by the Human Connectome Project (HCP), in combination with various deep artificial neural network models to perform brain decoding. More specifically, here we investigate to which extent can we infer the task performed by a subject based on its MEG data. Three models based on compact convolution, combined convolutional and long short-term architecture as well as a model based on multi-view learning that aims at fusing the outputs of the two stream networks are proposed and examined. These models exploit the spatio-temporal MEG data for learning new representations that are used to decode the relevant tasks across subjects. In order to realize the most relevant features of the input signals, two attention mechanisms, i.e. self and global attention, are incorporated in all the models. The experimental results of cross subject multi-class classification on the studied MEG dataset show that the inclusion of attention improves the generalization of the models across subjects.
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Picower Institute for Learning and Memory（皮考尔学习与记忆研究所）；MIT（麻省理工学院）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
Brain source imaging is an important method for noninvasively characterizing brain activity using Electroencephalogram (EEG) or Magnetoencephalography (MEG) recordings. Traditional EEG/MEG Source Imaging (ESI) methods usually assume that either source activity at different time points is unrelated, or that similar spatiotemporal patterns exist across an entire study period. The former assumption makes ESI analyses sensitive to noise, while the latter renders ESI analyses unable to account for time-varying patterns of activity. To effectively deal with noise while maintaining flexibility and co…
An Analysis of How Spatiotemporal Dynamic Models of Brain Activity Could Improve MEG/EEG Inverse Solutions
脑磁图和脑电图是非侵入性的功能性神经成像技术，提供了高时间分辨率的大脑活动记录，从而提供了一个独特的窗口，研究快速的时间尺度在人类的神经动力学。然而，从这些数据得到的大脑活动估计的准确性是有限的，主要是因为1）传感器的数量远小于源的数量，以及2）记录设备对深或径向取向的源的低灵敏度。这些因素限制了可以恢复的源的数量，并且使估计偏向于表层皮层区域，导致需要包括关于源活动的先验信息。如何具体说明这一信息以及如何改进解决方案的问题仍然是一个关键的未决问题。在本文中，我们表明，纳入知识的大脑的底层连接和时空动态可以显着提高逆解。为此，我们开发的概念的\textit{dynamic lead field mapping}，它表示如何在一个给定的时间源活动的信息不仅被映射到即时测量，但测量的时间序列。有了这个映射，我们表明，可以恢复的源参数的数量可以增加到一个因素的${\sim20}$，这种改善主要是由深层皮质区。我们的研究结果意味着未来的发展，在MEG/EEG分析模型时空动态有可能显着提高源分辨率。
Inserm（法国国家健康与医学研究院）；Paris Diderot University（巴黎第七大学）；Lariboisière Hospital（拉里博瓦西耶医院）；Assistance Publique Hôpitaux de Paris（巴黎公共援助医院）；Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）
Magnetoencephalography and electroencephalography (M/EEG) can reveal neuronal dynamics non-invasively in real-time and are therefore appreciated methods in medicine and neuroscience. Recent advances in modeling brain-behavior relationships have highlighted the effectiveness of Riemannian geometry for summarizing the spatially correlated time-series from M/EEG in terms of their covariance. However, after artefact-suppression, M/EEG data is often rank deficient which limits the application of Riemannian concepts. In this article, we focus on the task of regression with rank-reduced covariance ma…
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common assumption. It is often taken into account using convex constraints based on the l1-norm. The resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection due to high correlations in…
MEG/EEG are non-invasive imaging techniques that record brain activity with high temporal resolution. However, estimation of brain source currents from surface recordings requires solving an ill-posed inverse problem. Converging lines of evidence in neuroscience, from neuronal network models to resting-state imaging and neurophysiology, suggest that cortical activation is a distributed spatiotemporal dynamic process, supported by both local and long-distance neuroanatomic connections. Because spatiotemporal dynamics of this kind are central to brain physiology, inverse solutions could be impro…
McGovern Institute for Brain Research, Massachusetts Institute of Technology（麻省理工学院麦戈文脑研究所）；Complutense University of Madrid（马德里康普顿斯大学）；Harvard Medical School（哈佛医学院）
Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivit…
Inria Saclay（法国国家信息与自动化研究所萨克雷中心）；Université de Montpellier（蒙彼利埃大学）；CEA（法国原子能和替代能源委员会）
Detecting where and when brain regions activate in a cognitive task or in a given clinical condition is the promise of non-invasive techniques like magnetoencephalography (MEG) or electroencephalography (EEG). This problem, referred to as source localization, or source imaging, poses however a high-dimensional statistical inference challenge. While sparsity promoting regularizations have been proposed to address the regression problem, it remains unclear how to ensure statistical control of false detections. Moreover, M/EEG source imaging requires to work with spatio-temporal data and autocorr…
BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals
Shanghai Artificial Intelligence Laboratory, China（上海人工智能实验室）；Department of Electronic Engineering, Tsinghua University, China（清华大学电子工程系）；Department of Psychology, University of Cambridge, UK（剑桥大学心理学系）；Speech Hearing and Phonetic Sciences, University College London, UK（伦敦大学学院语音听力与语音科学系）；MRC Cognition and Brain Sciences Unit, University of Cambridge, UK（剑桥大学MRC认知与脑科学单位）
University of New Brunswick（新不伦瑞克大学）；University of Victoria（维多利亚大学）；National Research Council Canada（加拿大国家研究委员会）
Critically endangered species in Canadian North Atlantic waters are systematically surveyed to estimate species populations which influence governing policies. Due to its impact on policy, population accuracy is important. This paper lays the foundation towards a data-driven glare modelling system, which will allow surveyors to preemptively minimize glare. Surveyors use a detection function to estimate megafauna populations which are not explicitly seen. A goal of the research is to maximize useful imagery collected, to that end we will use our glare model to predict glare and optimize for gla…
Department of Neuroscience and Biomedical Engineering, Aalto University School of Science（阿尔托大学理学院神经科学与生物医学工程系）；Aalto NeuroImaging, Aalto University（阿尔托大学阿尔托神经成像中心）；Aalto University School of Science（阿尔托大学理学院）；Aalto University（阿尔托大学）
Convolutional Neural Networks (CNN) outperform traditional classification methods in many domains. Recently these methods have gained attention in neuroscience and particularly in brain-computer interface (BCI) community. Here, we introduce a CNN optimized for classification of brain states from magnetoencephalographic (MEG) measurements. Our CNN design is based on a generative model of the electromagnetic (EEG and MEG) brain signals and is readily interpretable in neurophysiological terms. We show here that the proposed network is able to decode event-related responses as well as modulations …
Directorate of Technology and Innovation（技术与创新局）；Chamber of Deputies（众议院）
Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex. We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project. In contrast to previous findings in the literature (Khaligh-Razavi &Kriegeskorte, …
How meaning is represented in the brain is still one of the big open questions in neuroscience. Does a word (e.g., bird) always have the same representation, or does the task under which the word is processed alter its representation (answering "can you eat it?" versus "can it fly?")? The brain activity of subjects who read the same word while performing different semantic tasks has been shown to differ across tasks. However, it is still not understood how the task itself contributes to this difference. In the current work, we study Magnetoencephalography (MEG) brain recordings of participants…
Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines
Universiti Teknologi PETRONAS（马来西亚国油大学）；Hospital Universiti Sains Malaysia（马来西亚理科大学医院）；Universiti Sains Malaysia（马来西亚理科大学）；Ulster University（阿尔斯特大学）；University of Calgary（卡尔加里大学）；University of Bath（巴斯大学）
本文基于 324 名 BioFIND 参与者的 MEG 与 MRI 数据，通过交叉验证机器学习分类识别 MCI，发现融合未校正 MEG 与 z-score 校正 MRI 特征效果最佳。
Facebook AI Research（脸书人工智能研究院）；ENSAM（巴黎高等工程技术学院）；Université Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；École normale supérieure, PSL University（巴黎文理研究大学高等师范学院）；CNRS（法国国家科学研究中心）
Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they capture highly nonlinear dynamics. These challenges have led the community to develop a variety of preprocessing and analytical (almost exclusively linear) methods, each designed to tackle one of these issues. Instead, we propose to address these challenges through a specific end-to-end deep learning architecture, trained to predict the brain responses of multiple subjects at once. We successfully test this approach on…
Brain signals constitute the information that are processed by millions of brain neurons (nerve cells and brain cells). These brain signals can be recorded and analyzed using various of non-invasive techniques such as the Electroencephalograph (EEG), Magneto-encephalograph (MEG) as well as brain-imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and others, which will be discussed briefly in this paper. This paper discusses about the currently emerging techniques such as the usage of different Deep Learning (DL) algorithms for the analysis of these brain sign…
MEG-GPT: A transformer-based foundation model for magnetoencephalography data
Oxford Centre for Integrative Neuroimaging (OxCIN)（牛津整合神经影像中心）；University of Oxford（牛津大学）；Department of Psychiatry（精神病学系）；Nuffield Department of Clinical Neurosciences（努尔菲尔德临床神经科学系）；Department of Engineering Science（工程科学系）
本文提出 MEG-GPT，一种基于 transformer、时间注意力和下一时间点预测的 MEG 基础模型，并通过无损数据驱动分词器实现真实脑信号生成与跨会话、跨受试者零样本解码提升。
GPT2MEG: Quantizing MEG for Autoregressive Generation
Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK（牛津大学心理系人类大脑活动中心）；Wellcome Centre for Integrative Neuroimaging, Oxford, UK（牛津大学整合神经影像研究中心）；Department of Engineering Science, University of Oxford, Oxford, UK（牛津大学工程科学系）；Foresight Institute（前瞻研究所）
The detection of human sleep stages is widely used in the diagnosis and intervention of neurological and psychiatric diseases. Some patients with deep brain stimulator implanted could have their neural activities recorded from the deep brain. Sleep stage classification based on deep brain recording has great potential to provide more precise treatment for patients. The accuracy and generalizability of existing sleep stage classifiers based on local field potentials are still limited. We proposed an applicable cross-modal transfer learning method for sleep stage classification with implanted de…
Diffusion-based translation between unpaired spontaneous premature neonatal EEG and fetal MEG
Laboratoire de Mathématiques de Lens (LML), UR 2462, Université d'Artois（Lens数学实验室（LML），UR 2462，阿托瓦大学）；Department of Internal Medicine IV, University Hospital of Tübingen（第四内科部门，图宾根大学医院）；IDM/fMEG Center of the Helmholtz Center Munich, University of Tübingen, German Center for Diabetes Research (DZD)（慕尼黑海德堡中心IDM/fMEG中心，图宾根大学，糖尿病研究德国中心（DZD））；Institute of Pharmaceutical Sciences, Department of Pharmacy and Biochemistry, Interfaculty Centre for Pharmacogenomics and Pharma Research, Eberhard Karls University Tübingen（药学研究所，药学与生物化学部门，药基因组学与药学研究跨学科中心，图宾根埃伯哈德·卡尔斯大学）
Decoding individual words from non-invasive brain recordings across 723 participants
Meta AI；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；CEA（法国原子能和替代能源委员会）；Neurospin center（神经自旋中心）；École Normale Supérieure（巴黎高等师范学院）；PSL University（巴黎文理研究大学）；Laboratoire des Systèmes Perceptifs（感知系统实验室）
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Brain connectivity networks, which characterize the functional or structural interaction of brain regions, has been widely used for brain disease classification. Kernel-based method, such as graph kernel (i.e., kernel defined on graphs), has been proposed for measuring the similarity of brain networks, and yields the promising classification performance. However, most of graph kernels are built on unweighted graph (i.e., network) with edge present or not, and neglecting the valuable weight information of edges in brain connectivity network, with edge weights conveying the strengths of temporal…
MEG-to-MEG Transfer Learning and Cross-Task Speech/Silence Detection with Limited Data
HiTZ Center, University of the Basque Country -- UPV/EHU, Spain（比斯开国家大学HiTZ中心）；Basque Center on Cognition, Brain and Language -- BCBL, Spain（巴斯克认知、大脑与语言中心）；Ikerbasque, Basque Foundation for Science, Spain（巴斯克科学基金会Ikerbasque）；Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB), Belgium（布鲁塞尔自由大学人体运动科学系功能解剖实验室）；Laboratoire de Neuroanatomie et Neuroimagerie translationnelles (LN2T), ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Belgium（布鲁塞尔自由大学神经解剖与神经影像翻译实验室）；WEL Research Institute, Belgium（比利时WEL研究机构）
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis, head segmentation is commonly used for measuring and visualizing the brain's anatomical structures and is also a necessary step for other applications such as current-source reconstruction in electroencephalography and magnetoencephalography (EEG/MEG). Here we propose a deep learning framework that can segment brain, skull, and extra-cranial tissue using only T1-weighted MRI as input. In addition, we describe a rob…
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Automotive Engineering Research Institute, Jiangsu University（江苏大学汽车工程研究院）；Stanford University（斯坦福大学）；School of Mathematics Science, Liaocheng University（聊城大学数学科学学院）；School of Electric Engineering, Zhengzhou University（郑州大学电气工程学院）；Korea University（高丽大学）
Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Network Construction and Classification" (BrainNetClass) to the field to promote more advanced brain network construction methods. It comprises various brain network construction methods, including some state-of-the-art methods that were recently develop…
Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data
Lyon Neuroscience Research center（里昂神经科学研究中心）；INSERM（法国国家健康与医学研究院）；Functional Neurology and Epileptogy, HCL（里昂公立医院集团功能神经与癫痫科）；Univ Lyon（里昂大学）；CNRS（法国国家科学研究中心）；INSA Lyon（里昂国立应用科学学院）；UCBL（里昂第一大学）；CREATIS（里昂成像与生物技术研究中心）
Decoding Phone Pairs from MEG Signals Across Speech Modalities
HiTZ Center, University of the Basque Country -- UPV/EHU（巴斯克国家大学希茨中心）；Basque Centre on Cognition, Brain and Language -- BCBL（巴斯克认知、大脑与语言中心）；Laboratory of Functional Anatomy, Faculty of Human Motor Sciences, Université libre de Bruxelles (ULB)（人类运动科学系功能解剖实验室）；Laboratoire de Neuroanatomie et Neuroimagerie translationnelles (LN2T), ULB Neuroscience Institute, Université libre de Bruxelles (ULB)（神经解剖与神经影像翻译实验室（LN2T）、ULB神经科学研究所）；WEL Research Institute（WEL研究机构）
University of Cambridge（剑桥大学）；Shanghai General Hospital, Shanghai Jiao Tong University（上海交通大学附属第一人民医院）；University of Bath（巴斯大学）
Alzheimer's disease (AD) is the most common age-related dementia. It remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a noninvasive biomarker to detect brain aging. Previous evidence shows that the brain structural change detected by diffusion MRI is associated with dementia. Mounting studies has conceptualised the brain as a complex network, which has shown the utility of this approach in characterising various neurological and psychiatric disorders. Therefore, the structural connectivity shows promise in dementia classification. The…
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…
Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification
Institute of Innovation, Science, and Sustainability（创新、科学与可持续性研究所）；Federation University Australia（联邦大学澳大利亚分校）；Stainaz Research（斯泰纳兹研究）；MIoT & IPIN Lab（MIoT与IPIN实验室）；University of New South Wales（新南威尔士大学）；School of Information and Electronic Engineering（信息与电子工程学院）；Zhejiang Gongshang University（浙江工商大学）；School of Computing Technologies（计算技术学院）
Faculty of Information Technology, University of Science, VNU-HCM（越南胡志明市国家大学科学大学信息技术学院）；SBEI School №1228 ”Lefortovo”（第1228号“列福尔托沃”基础普通教育学校）
In the development of technology, there are increasing cases of brain disease, there are more treatments proposed and achieved a positive result. However, with Brain-Lesion, the early diagnoses can improve the possibility for successful treatment and can help patients recuperate better. From this reason, Brain-Lesion is one of the controversial topics in medical images analysis nowadays. With the improvement of the architecture, there is a variety of methods that are proposed and achieve competitive scores. In this paper, we proposed a technique that uses efficient-net for 3D images, especiall…
