Understanding our brain is one of the most daunting tasks, one we cannot expect to complete without the use of technology. MindBigData aims to provide a comprehensive and updated dataset of brain signals related to a diverse set of human activities so it can inspire the use of machine learning algorithms as a benchmark of 'decoding' performance from raw brain activities into its corresponding (labels) mental (or physical) tasks. Using commercial of the self, EEG devices or custom ones built by us to explore the limits of the technology. We describe the data collection procedures for each of the sub datasets and with every headset used to capture them. Also, we report possible applications in the field of Brain Computer Interfaces or BCI that could impact the life of billions, in almost every sector like healthcare game changing use cases, industry or entertainment to name a few, at the end why not directly using our brains to 'disintermediate' senses, as the final HCI (Human-Computer Interaction) device? simply what we call the journey from Type to Touch to Talk to Think.
Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China（华中科技大学人工智能与自动化学院，武汉，中国）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）
Quantum-Brain: Quantum-Inspired Neural Network Approach to Vision-Brain Understanding
Department of Electrical Engineering and Computer Science, University of Arkansas（电气工程与计算机科学系，阿肯色大学）；Department of Physics, University of Arkansas（物理系，阿肯色大学）；Department of Radiology, SUNY Upstate Medical University（放射学系，SUNY Upstate医学大学）；Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology（脑科学与认知科学系，麻省理工学院）；Department of Electrical and Computer Engineering, Kansas State University（电气与计算机工程系，堪萨斯州立大学）
Cogni-Net: Cognitive Feature Learning through Deep Visual Perception
Nanyang Technological University（南洋理工大学）；Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
我们能让计算机识别我们仅仅从大脑信号中看到的东西吗？我们的论文旨在利用流行的预训练的视觉模型在视觉领域学习的知识，并用它来教一个经常性的模型被训练的大脑信号学习的区别性的多方面的人类大脑的认知不同的视觉对象类别响应感知的视觉线索。为此，我们利用从视觉刺激（如图像）触发的脑EEG信号，并利用图像及其相应的脑信号之间的自然同步来学习认知特征空间的新表示。知识蒸馏的概念已在这里用于训练的深层认知模型，认知网\footnote{The source code of the proposed system is publicly available at {https://www.github.com/53X/CogniNET}}，采用学生-教师学习技术，以桥梁的过程中，跨模态的知识转移。所提出的新架构获得了最先进的结果，显着超过其他现有的模型。我们进行的实验还表明，如果可以大规模收集大脑EEG信号等视觉刺激信息，那么这将有助于更好地了解人类大脑认知的大部分未开发领域。
A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
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）
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）
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）
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）
Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communication
School of Electrical and Data Engineering, University of Technology Sydney（悉尼技术大学电气与数据工程学院）；Thrust of Internet of Things, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）物联网研究所）
An Analysis Scheme for Investigation of Effects of Various Parameters on Signals in Acoustic-Resolution Photoacoustic Microscopy of Mice Brain: a Simulation Study
EEG2MOTION: Towards Open-Vocabulary Human Motion Synthesis from Non-invasive Brain Signals
Zhejiang University（浙江大学）；Qiushi Academy for Advanced Studies, Zhejiang University（浙江大学求是高等研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；The Hong Kong Polytechnic University（香港理工大学）；Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University（浙江大学教育部生物医学工程重点实验室）
Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective
Department of Electrical and Computer Engineering at the University at Albany, SUNY（纽约州立大学阿尔巴尼分校电气与计算机工程系）；Department of Electrical and Computer Engineering at the University of Rochester（罗切斯特大学电气与计算机工程系）；Department of Electrical and Systems Engineering at the University of Pennsylvania（宾夕法尼亚大学电气与系统工程系）
