Autonomous Intelligent Machines & Systems

自主智能机器

基本信息

批准号：
2880664
负责人：
金额：
--
依托单位：
University of Oxford
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2880664
关键词：
Autonomous Intelligent Machines Systems

项目摘要

Research Proposal: Decoding Language from Non-Invasive Brain Activity using Machine LearningMy research focuses on advancing machine learning models to decode language from brain activity, specifically using non-invasive techniques such as functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Despite recent advancements, current models struggle with poor generalization to unseen linguistic contexts, especially across novel participants and diverse language inputs. This limitation highlights the need for more robust models capable of handling complex and variable brain data. Decoding language from brain signals presents significant challenges, because of the high-dimensional and noisy nature of the data, as well as substantial individual variability in brain responses. Therefore, my research specifically aims to address the following fundamental questions: How and why do brain signals differ between individuals? How can decoding models account for these individual differences while ensuring performance on unseen participants? I am particularly interested in developing methods that learn meaningful representations of participants, enabling the clustering and understanding of similar participants, improving our understanding of differences and the model's ability to generalize across diverse populations. Through this, I hope to contribute to the development of more accurate and adaptive brain decoding models that bridge the gap between brain activity and language.

研究建议：使用机器学习的非侵入性大脑活动中解码语言的重点是推进机器学习模型，以从大脑活动中解码语言，特别是使用非侵入性技术（例如功能磁共振成像（FMRI）和磁脑摄影（MEG））进行的。尽管有最近的进步，但当前的模型却在泛化的语言环境中挣扎着不良的概括，尤其是在新颖的参与者和多样化的语言意见之间。这种限制突出了需要更健壮的模型，能够处理复杂和可变的大脑数据。由于数据的高维和嘈杂性以及大脑反应的实质性变异性，大脑信号解码语言提出了重大挑战。因此，我的研究特别旨在解决以下基本问题：个人之间的大脑信号如何以及为什么不同？解码模型如何在确保看不见的参与者的表现的同时解释这些个体差异？我对开发学习参与者的有意义表示的方法特别感兴趣，从而实现了类似参与者的聚类和理解，提高了我们对差异的理解以及模型在不同人群中概括的能力。通过此，我希望为开发更准确和适应性的大脑解码模型做出贡献，这些模型弥合了大脑活动和语言之间的差距。