Statistical signal processing for non- invasive neurological data

非侵入性神经数据的统计信号处理

• 批准号: 312490-2011
• 负责人: Wang, ZJane
• 金额: $ 2.91万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=595102
• 关键词: Statistical; signal; processing; non; invasive

Biomedical Technologies (BT) is identified as a key strategic area in Canada, and developing advanced signal processing tools is vital for the advancement of BT. The amount and thus analysis complexity of non-invasive neurological data, such as scalp electroencephalography (EEG), surface electromyography (EMG) and functional magnetic resonance imaging (fMRI), is expected to increase, both with advances in medical hardware and increasing pressures from an aging population. The proposed research continues the applicant's work on statistical signal processing theory and applications, with the long term goal of developing novel, fundamental signal and information processing theory and methods that will have a lasting impact on the field of neurological data analysis. We will address key challenges in real-world problems such as assessing brain connectivity by exploring several fundamental signal processing topics: a.) efficient compression methods, with focus on sensor compression, for compressing multichannel EEG/EMG/fMRI data; b.) frameworks for automatic artifact removal/correction in fMRI, EEG and simultaneous EEG-fMRI respectively; c.) graphical models (e.g. dynamic Bayesian networks, exact Bayesian and robust LASSOs) for accurately inferring brain connectivity from neurological data incorporating key challenges, including sparsity, temporal-variability, error control and group analysis; d.) novel data-driven fusion frameworks for multimodal data analysis; e.) sensitivity analysis to study how individual elements contribute to the overall network function, and explore optimal control theory for the control/intervention of the network dynamics.

生物医学技术（BT）被确定为加拿大的关键战略领域，开发先进的信号处理工具对于BT的发展至关重要。随着医疗硬件的进步和人口老龄化的压力增加，预计非侵入性神经学数据(如头皮脑电图（EEG）、表面肌电图（EMG）和功能磁共振成像（fMRI）的数量和分析复杂性将会增加。该研究将继续申请人在统计信号处理理论和应用方面的工作，其长期目标是发展新的，基本的信号和信息处理理论和方法，这些理论和方法将对神经数据分析领域产生持久的影响。我们将通过探索几个基本的信号处理主题来解决现实世界问题中的关键挑战，例如评估大脑连通性：a)有效的压缩方法，重点是传感器压缩，用于压缩多通道EEG/EMG/fMRI数据；b) fMRI、EEG和同时进行的EEG-fMRI的自动伪影去除/校正框架；c)图形模型（例如动态贝叶斯网络，精确贝叶斯和鲁棒LASSOs），用于从神经学数据中准确推断大脑连接，包括关键挑战，包括稀疏性，时间变异性，误差控制和组分析；d)用于多模态数据分析的新型数据驱动融合框架；e)敏感性分析，研究单个元素对整体网络功能的贡献，探索网络动态控制/干预的最优控制理论。