Northwestern University（西北大学）；Shirley Ryan Ability Lab（雪莉·瑞安能力实验室）；University of Pennsylvania（宾夕法尼亚大学）；Columbia University（哥伦比亚大学）；Zuckerman Mind Brain Behavior Institute（扎克曼脑与行为研究所）
Joshua I. Glaser, Ari S. Benjamin, Raeed H. Chowdhury, Matthew G. Perich, Lee E. Miller, Konrad P. Kording
Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding performance. This tutorial describes how to effectively apply these algorithms for typical decoding problems. We provide descriptions, best practices, and code for applying common machine learning methods, including neural networks and gradient boosting. We also provide detailed comparisons of the performance of various methods at the task of decoding spiking activity in motor cortex, somatosensory cortex, and hippocampus. Modern methods, particularly neural networks and ensembles, significantly outperform traditional approaches, such as Wiener and Kalman filters. Improving the performance of neural decoding algorithms allows neuroscientists to better understand the information contained in a neural population and can help advance engineering applications such as brain machine interfaces.
A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions（深港脑科学研究院-深圳基础研究机构）；Shenzhen Technological Research Center for Primate Translational Medicine（深圳灵长类转化医学技术研究中心）；University of Chinese Academy of Sciences（中国科学院大学）
Towards Unified Neural Decoding with Brain Functional Network Modeling
Westlake University（西湖大学）；Zhejiang University School of Medicine First Affiliated Hospital（浙江大学医学院附属第一医院）；Westlake Institute for Optoelectronics（西湖光电研究院）
Generalizable, real-time neural decoding with hybrid state-space models
Mila – Quebec AI Institute（Mila – 魁北克人工智能研究院）；Université de Montréal（蒙特利尔大学）；Columbia University（哥伦比亚大学）；University of Pennsylvania（宾夕法尼亚大学）；Canada CIFAR AI Chair（加拿大CIFAR人工智能教席）
NeuroSketch: A Practical Design Recipe for Neural Decoding
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Shanghai Institute of Microsystem and Information Technology（上海微系统与信息工程研究所）
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 Hong Kong Polytechnic University（香港理工大学）；Hong Kong Baptist University（香港浸会大学）
Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding
University of Chinese Academy of Sciences（中国科学院大学）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
University of Rome, Tor Vergata Department of Biomedicine and Prevention（罗马大学Tor Vergata医学院与预防学院）；A.A. Martinos Center for Biomedical Imaging（A.A. Martinos生物医学成像中心）；Harvard Medical School/MGH, Boston (US)（哈佛医学院/麻省总医院，波士顿（美国））
Brain2Text Decoding Model Reveals the Neural Mechanisms of Visual Semantic Processing
Ministry of Education Center for Studies of Psychological Application（教育部心理应用研究中心）；Center for Studies of Psychological Application（心理应用研究中心）；Key Laboratory of Brain, Cognition and Education Sciences（脑认知与教育科学重点实验室）；School of Psychology（心理学学院）；Guangdong Key Laboratory of Mental Health and Cognitive Science（广东省心理健康与认知科学重点实验室）
Decoding Predictive Inference in Visual Language Processing via Spatiotemporal Neural Coherence
Department of Physics（物理系）；Carnegie-Mellon University（卡内基梅隆大学）；Department of Linguistics（语言学系）；University of Salzburg（萨尔茨堡大学）；Purdue University（普渡大学）；University of Alabama（阿拉巴马大学）；Department of Communicative Disorders（沟通障碍系）
