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 signals and how these algorithms will be helpful in determining the neurological status of a person by applying the signal decoding strategy.
Research Focus Cognitive Sciences（认知科学研究重点中心）；University of Potsdam（波茨坦大学）；Artificial Intelligence Lab（人工智能实验室）；Otto von Guericke University（奥托·冯·格里克大学）
We describe a framework of hybrid cognition by formulating a hybrid cognitive agent that performs hierarchical active inference across a human and a machine part. We suggest that, in addition to enhancing human cognitive functions with an intelligent and adaptive interface, integrated cognitive processing could accelerate emergent properties within artificial intelligence. To establish this, a machine learning part learns to integrate into human cognition by explaining away multi-modal sensory measurements from the environment and physiology simultaneously with the brain signal. With ongoing t…
Cross-Subject Generalization in Decoding Perceived Speech from Non-Invasive Brain Recordings
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院）；National Key Laboratory of General Artificial Intelligence（通用人工智能国家重点实验室）
Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA（计算与增强智能学院，亚利桑那州立大学）；Amazon AGI, Redmond, WA, USA（亚马逊人工智慧部门）；Banner Alzheimer’s Institute, Phoenix, AZ, USA（贝纳尔德阿尔茨海默病研究所）
Brain stimulation is a core method in neuroscience. Numerous non-invasive brain stimulation (NIBS) techniques are currently in use in basic and clinical research, and recent advances promise the ability to non-invasively access deep brain structures. While encouraging, there is a surprising gap in our understanding of precisely how NIBS perturbs neural activity throughout an interconnected network, and how such perturbed neural activity ultimately links to behaviour. In this review, we will consider why non-human primate (NHP) models of NIBS are ideally situated to address this gap in knowledg…
Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface
Institute of Artificial Intelligence（人工智能研究所）；M.V.Lomonosov Moscow State University（罗蒙诺索夫莫斯科国立大学）；Rebis LLC（Rebis有限责任公司）；Aicumene LLC（Aicumene有限责任公司）；Neurosputnik LLC（Neurosputnik有限责任公司）
In this study, we illustrate the progress of BCI research and present scores of unveiled contemporary approaches. First, we explore a decoding natural speech approach that is designed to decode human speech directly from the human brain onto a digital screen introduced by Facebook Reality Lab and University of California San Francisco. Then, we study a recently presented visionary project to control the human brain using Brain-Machine Interfaces (BMI) approach. We also investigate well-known electroencephalography (EEG) based Emotiv Epoc+ Neuroheadset to identify six emotional parameters inclu…
University of New South Wales（新南威尔士大学）；Harvard University（哈佛大学）；University of Technology Sydney（悉尼科技大学）；Macquarie university（麦考瑞大学）；Lehigh University（里海大学）
Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer interface systems significantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions most published in the past five years. Finally, we discus…
A Pre-trained Framework for Multilingual Brain Decoding Using Non-invasive Recordings
Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Baptist University（香港 Baptist大学）
Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings
Meta AI；École Normale Supérieure（高等师范学院）；Université PSL（巴黎文理大学）；CNRS（法国国家科学研究中心）；Hospital Foundation Adolphe de Rothschild（阿道夫·德·罗斯柴尔德医院基金会）；Univ. Lille（里尔大学）；Basque Center on Cognition, Brain and Language（巴斯克认知、大脑与语言中心）；Paris Cité University（巴黎城市大学）；Inria（法国国家信息与自动化研究所）；Université Paris-Saclay（巴黎萨克雷大学）；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）
A major open challenge in neuroscience is the ability to measure and perturb neural activity in vivo from well-defined neural sub-populations at cellular resolution anywhere in the brain. However, limitations posed by scattering and absorption prohibit non-invasive (surface) multiphoton approaches for deep (>2mm) structures, while Gradient Refreactive Index (GRIN) endoscopes are thick and cause significant damage upon insertion. Here, we demonstrate a novel microendoscope to image neural activity at arbitrary depths via an ultrathin multimode optical fiber (MMF) probe that is 5-10X thinner tha…
Nagoya Institute of Technology（名古屋工业大学）；Suez Canal University（苏伊士运河大学）
Electro-stimulation or modulation of deep brain regions is commonly used in clinical procedures for the treatment of several nervous system disorders. In particular, transcranial direct current stimulation (tDCS) is widely used as an affordable clinical application that is applied through electrodes attached to the scalp. However, it is difficult to determine the amount and distribution of the electric field (EF) in the different brain regions due to anatomical complexity and high inter-subject variability. Personalized tDCS is an emerging clinical procedure that is used to tolerate electrode …
Riemannian Geometry for the classification of brain states with intracortical brain-computer interfaces
John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；Faculty of Medicine and Health Sciences, University of Barcelona（巴塞罗那大学医学与健康科学学院）；Whiting School of Engineering, Johns Hopkins University（约翰斯·霍普金斯大学惠廷工程学院）；School of Mathematics and Statistics, Polytechnic University of Catalonia（加泰罗尼亚理工大学数学与统计学院）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝斯以色列女执事医疗中心）；Catalan Institution for Research and Advanced Studies (ICREA)（加泰罗尼亚高等研究院（ICREA））
Prenatal stress (PS) impacts early postnatal behavioural and cognitive development. This process of 'fetal programming' is mediated by the effects of the prenatal experience on the developing hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS). The HPA axis is a dynamic system regulating homeostasis, especially the stress response, and is highly sensitive to adverse early life experiences. We review the evidence for the effects of PS on fetal programming of the HPA axis and the ANS. We derive a multi-scale multi-species approach to devising preclinical and clinical stu…
Non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS) provides a powerful tool to directly modulate brain oscillations that mediate complex cognitive processes. While the body of evidence about the effect of tACS on behavioral and cognitive performance is constantly growing, those studies fail to address the importance of subject- specific stimulation protocols. With this study here, we set the foundation to combine tACS with a recently presented framework that utilizes real-time fRMI and Bayesian optimization in order to identify the most optimal tACS pro…
Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow
School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）；School of Smart Health, Chongqing Polytechnic University of Electronic Technology（重庆电子工程职业大学智能健康学院）
Multi-modal neuroimaging projects are advancing our understanding of human brain architecture, function, connectivity using high-quality non-invasive data from many subjects. However, ground truth validation of connectivity using invasive tracers is not feasible in humans. Our NonHuman Primate Neuroimaging & Neuroanatomy Project (NHP_NNP) is an international effort (6 laboratories in 5 countries) to: (i) acquire and analyze high-quality multi-modal brain imaging data of macaque and marmoset monkeys using protocols and methods adapted from the HCP; (ii) acquire quantitative invasive tract-traci…
BrainWave: A Brain Signal Foundation Model for Clinical Applications
Computer Science and Technology（计算机科学与技术）；Zhejiang University（浙江大学）；Chinese Academy of Sciences（中国科学院）；INSIDE Institute for Biological and Artificial Intelligence（生物与人工智能研究所）；Research Institute of Intelligent and Complex Systems（智能与复杂系统研究所）；Fudan University（复旦大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Chicago（芝加哥大学）
High precision neuromodulation is a powerful tool to decipher neurocircuits and treat neurological diseases. Current non-invasive neuromodulation methods offer limited precision at the millimeter level. Here, we report optically-generated focused ultrasound (OFUS) for non-invasive brain stimulation with ultrahigh precision. OFUS is generated by a soft optoacoustic pad (SOAP) fabricated through embedding candle soot nanoparticles in a curved polydimethylsiloxane film. SOAP generates a transcranial ultrasound focus at 15 MHz with an ultrahigh lateral resolution of 83 um, which is two orders of m…
While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals. In this work it is shown that index finger positions can be differentiated from non-invasive electroencephalographic (EEG) recordings in healthy human subjects. Using a leave-one-subject-out cross-validation procedure, a random forest distinguished different index finger positions on a numerical keyboard above chance-level accuracy. Among the different sp…
We propose a fusion approach that combines features from simultaneously recorded electroencephalographic (EEG) and magnetoencephalographic (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of non-inv…
The effects of temperature on various aspects of neural activity from single cell to neural circuit level have long been known. However, how temperature affects the system-level of activity typical of experiments using non-invasive imaging techniques, such as magnetic brain imaging of electroencephalography, where neither its direct measurement nor its manipulation are possible, is essentially unknown. Starting from its basic physical definition, we discuss possible ways in which temperature may be used both as a parameter controlling the evolution of other variables through which brain activi…
Spontaneous Spatial Cognition Emerges during Egocentric Video Viewing through Non-invasive BCI
Hangzhou Dianzi University（杭州电子科技大学）；Zhejiang University（浙江大学）；RIKEN Center for Advanced Intelligence Project（日本理化学研究所先进智能项目中心）；RIKEN（日本理化学研究所）；Systems Research Institute（系统研究所）；Polish Academy of Sciences（波兰科学院）；Nicolaus Copernicus University（尼古拉哥尔斯基大学）；University of Rome Sapienza（罗马萨皮恩扎大学）
Centre for Computational Intelligence, University of Essex（埃塞克斯大学计算智能中心）
The last decades have seen significant advancements in non-invasive neuroimaging technologies that have been increasingly adopted to examine human brain development. However, these improvements have not necessarily been followed by more sophisticated data analysis measures that are able to explain the mechanisms underlying functional brain development. For example, the shift from univariate (single area in the brain) to multivariate (multiple areas in brain) analysis paradigms is of significance as it allows investigations into the interactions between different brain regions. However, despite…
Standard neuroimaging techniques provide non-invasive access not only to human brain anatomy but also to its physiology. The activity recorded with these techniques is generally called functional imaging, but what is observed per se is an instance of dynamics, from which functional brain activity should be extracted. Distinguishing between bare dynamics and genuine function is a highly non-trivial task, but a crucially important one when comparing experimental observations and interpreting their significance. Here we illustrate how the ability of neuroimaging to extract genuine functional brai…
University Medical Center Freiburg（弗莱堡大学医学中心）；Alberts-Ludwigs-University Freiburg（阿尔伯特-路德维希-弗莱堡大学）
We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learning for error decoding with deep convolutional neural networks (deep ConvNets). In comparison with a regularized linear discriminant analysis (rLDA) classifier, ConvNets were significantly better in both intra- and inter-subject decoding, achieving an average accuracy of 84.1 % within subject and 81.7 % on unknown subjects (flanker task). Neither method was, however, able to generalize reliably between paradigms. Visua…
Deep brain stimulation(DBS)has become an effective intervention for advanced Parkinson's disease, but the exact mechanism of DBS is still unclear. In this review, we discuss the history of DBS, the anatomy and internal architecture of the basal ganglia(BG), the abnormal pathological changes of the BG in Parkinson's disease, and how computational models can help understand and advance DBS. We also describe two types of models:mathematical theoretical models and clinical predictive models. Mathematical theoretical models simulate neurons or neural networks of BG to shed light on the mechanistic …
To understand Working of Human Brain measurements related to the brain function are required. These measurements should be possibly non-invasive. Brain should be disturbed as less as possible during the measurement. Integration of various modalities plays a vital role in understanding the cognitive and the behavioral changes in the human brain. It is an important source of converging evidence about specific aspects of neural functions and dysfunctions under certain pathological conditions. Focal changes in cortical blood flow are tightly coupled with the changes in neuronal activity. This cons…
Du-IN: Discrete units-guided mask modeling for decoding speech from Intracranial Neural signals
Peking University（北京大学）；Chinese Institute for Brain Research（中国脑科学研究院）；Beijing Normal University（北京师范大学）；Shanghai Jiao Tong University（上海交通大学）；Capital Medical University（首都医科大学）；Xuanwu Hospital（宣武医院）
This article serves as a supplement to the recently published call for participation in a Research Topic [1] that is timed to commemorate the 75th anniversary of Shannon's pioneering 1948 paper [2]. Here, we include some citations of key and relevant literature, which reflect our opinions/perspectives on the proposed topic, and serve as guidance to potential submissions.
Light-sheet fluorescence microscopy (LSFM) enables real-time whole-brain functional imaging in zebrafish larvae. Conventional one photon LSFM can however induce undesirable visual stimulation due to the use of visible excitation light. The use of two-photon (2P) excitation, employing near-infrared invisible light, provides unbiased investigation of neuronal circuit dynamics. However, due to the low efficiency of the 2P absorption process, the imaging speed of this technique is typically limited by the signal-to-noise-ratio. Here, we describe a 2P LSFM setup designed for non-invasive imaging th…
