Rybář Milan, Daly Ian
Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom.
J Neural Eng. 2022 Apr 13;19(2). doi: 10.1088/1741-2552/ac619a.
Semantic concepts are coherent entities within our minds. They underpin our thought processes and are a part of the basis for our understanding of the world. Modern neuroscience research is increasingly exploring how individual semantic concepts are encoded within our brains and a number of studies are beginning to reveal key patterns of neural activity that underpin specific concepts. Building upon this basic understanding of the process of semantic neural encoding, neural engineers are beginning to explore tools and methods for semantic decoding: identifying which semantic concepts an individual is focused on at a given moment in time from recordings of their neural activity. In this paper we review the current literature on semantic neural decoding.We conducted this review according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines. Specifically, we assess the eligibility of published peer-reviewed reports via a search of PubMed and Google Scholar. We identify a total of 74 studies in which semantic neural decoding is used to attempt to identify individual semantic concepts from neural activity.Our review reveals how modern neuroscientific tools have been developed to allow decoding of individual concepts from a range of neuroimaging modalities. We discuss specific neuroimaging methods, experimental designs, and machine learning pipelines that are employed to aid the decoding of semantic concepts. We quantify the efficacy of semantic decoders by measuring information transfer rates. We also discuss current challenges presented by this research area and present some possible solutions. Finally, we discuss some possible emerging and speculative future directions for this research area.Semantic decoding is a rapidly growing area of research. However, despite its increasingly widespread popularity and use in neuroscientific research this is the first literature review focusing on this topic across neuroimaging modalities and with a focus on quantifying the efficacy of semantic decoders.
语义概念是我们头脑中连贯的实体。它们支撑着我们的思维过程,是我们理解世界的部分基础。现代神经科学研究越来越多地探索个体语义概念是如何在我们大脑中编码的,并且一些研究开始揭示支撑特定概念的神经活动关键模式。基于对语义神经编码过程的这一基本理解,神经工程师们开始探索语义解码的工具和方法:从个体神经活动记录中识别出其在给定时刻所关注的语义概念。在本文中,我们回顾了关于语义神经解码的当前文献。我们按照系统评价和荟萃分析的首选报告项目(PRISMA)指南进行了此项综述。具体而言,我们通过搜索PubMed和谷歌学术来评估已发表的同行评审报告的合格性。我们总共识别出74项研究,其中使用语义神经解码试图从神经活动中识别个体语义概念。我们的综述揭示了现代神经科学工具是如何被开发出来以便从一系列神经成像模态中解码个体概念的。我们讨论了用于辅助语义概念解码的具体神经成像方法、实验设计和机器学习流程。我们通过测量信息传递速率来量化语义解码器的功效。我们还讨论了该研究领域目前面临的挑战并提出了一些可能的解决方案。最后,我们讨论了该研究领域一些可能出现的和推测性的未来方向。语义解码是一个快速发展的研究领域。然而,尽管它在神经科学研究中的应用越来越广泛且受到欢迎,但这是第一篇聚焦于跨神经成像模态且着重量化语义解码器功效的关于此主题的文献综述。