System Identification Software for Cognitive Electrophysiology

描述(由申请人提供)：本提案中的工作将开发并向研究人员提供新的工具，用于研究与事件相关的人类电生理信号的时变变化，例如由于学习、习惯化、易化、适应或药物干预而可能发生的变化。传统的平均事件相关电位和磁场允许以毫秒级的分辨率对潜在的大脑过程进行非侵入性探索，但不允许以直接的方式对事件之间的相互作用进行定量分析。本提案中描述的工作将实施实验任务相关系统识别(Volterra)方法，使我们能够定量描述事件的线性响应(与常规平均波形密切相关)和事件相互作用的非线性动态(不是通过常规平均处理的)。算法将被开发、体现在计算机软件中，并使用模拟和实验数据进行评估。生成的软件将成为我们的EMSE套件商业软件包的一部分。对这些时变变化的更全面的理解将扩展我们在基础认知神经科学及其临床应用方面的知识，如精神分裂症和其他脑动力学障碍。

DESCRIPTION (provided by applicant): The work in this proposal will develop and make available to researchers new tools for studying time-varying changes in event-related human electrophysiological signals, such as those that might occur due to learning, habituation, facilitation, adaptation, or pharmacological intervention. Conventional average event- related electrical potentials and magnetic fields permit non-invasive exploration of underlying brain processes with millisecond resolution, but do not allow the quantitative analysis of interactions between events in a straightforward way. The work described in this proposal will implement experimental task- related system identification (Volterra) methods that will permit us to describe quantitatively both the linear responses to events (which are related closely to conventional average waveforms) and the non-linear dynamics of event interactions (which are not addressed by conventional averaging). Algorithms will be developed, embodied in computer software, and evaluated using simulated and experimental data. The resulting software will form part of our EMSE Suite commercial software package. A fuller understanding of these time varying changes will extend our knowledge in basic cognitive neuroscience, as well as its clinical applications, such as schizophrenia and other disorders of brain dynamics.