Using Nonlinear Visual Stimulation and Dynamical Data Analysis to Investigate the Temporal Characteristics of Information Processing in Human Visual System(2/2)

Project Details


Our brain processes internal representation and external information in a dynamic, nonstationary and nonlinear manner. This collective neural processes and oscillations can be studied with great temporal resolution through the use of electroencephalography (EEG). However, current analytical methods for studying the neural dynamics although offering extensive information they do not fulfill the requisites for studying a nonstationary and nonlinear signal only partially characterizing it. In this series of experiment we plan to holistically explore the nonlinear characteristic of neural mechanisms in the visual system. In light of this, we intend to stimulate the visual system with a variety of flicker stimulation conditions and study the Steady State Visual Evoked Potential (SSVEP) of EEG recordings using novel analytical methods that are more accurate and offer a comprehensive view of the nonstationary and non-linear components involved in neural processing. First, we will employ multiple analytical tools including Holo-Hilbert Spectral analysis (HHSA, Huang 2016) a recently developed method that can analyze inter-wave processes to discern the way neurons communicate with each other. We expect to observe the nonstationary and non-linear signal characteristics as well as their frequency coupling components. We intend to develop and estimate the visual latency response with a precise analytical method. Second, we intend to develop and estimate the visual latency response with the analytical method. Third, we plan to test these new analytical tools in conjunction with non-invasive techniques for the study of patients with neurological disorders. Our aim is to build the bases for a new way of analyzing dynamical networks including a more precise response latency detection method as well as a better understanding and visualization of neural coupling mechanisms. The novel methods used in this project will offer knowledge to advance in the manners and mechanisms of neural oscillations and to gradually construct a corresponding theory for interpretation of nonlinear neural activities in the brain.
Effective start/end date1/08/1931/10/19

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 12 - Responsible Consumption and Production
  • SDG 17 - Partnerships for the Goals


  • Visual System
  • Nonlinear Neuronal Activities
  • Steady-State Visual Evoked Potential (SSVEP)
  • Holo-Hilbert Spectral Analysis
  • (HHSA)


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