Statistical signal processing for non- invasive neurological data

生物医学技术(BT)被确定为加拿大的一个关键战略领域，而开发先进的信号处理工具对促进生物医学技术的发展至关重要。随着医疗硬件的进步和人口老龄化带来的压力增加，非侵入性神经数据，如头皮脑电(EEG)、表面肌电(EMG)和功能磁共振成像(FMRI)的数量和分析复杂性预计将增加。拟议的研究继续申请者在统计信号处理理论和应用方面的工作，长期目标是开发将对神经数据分析领域产生持久影响的新的、基本的信号和信息处理理论和方法。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，例如评估大脑连接：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和同步EEG-fMRI中的伪影自动去除/校正框架；c.)图形模型(如动态贝叶斯网络、精确贝叶斯和稳健的套索)，用于从包含关键挑战的神经数据中准确推断大脑连接，包括稀疏性、时变性、差错控制和群体分析；d.用于多模式数据分析的数据驱动的新融合框架；e)敏感度分析，研究个体因素对网络整体功能的贡献，探索网络动态控制/干预的最优控制理论。

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.