近年來，關於大腦如何整合內在與外部訊息來形成認知經驗及行為反應，已逐漸從早期的模組取向轉移到動態、大尺度的系統取向。這種瞬時、集體的神經活化過程會造成大腦附近的局部電場變化，讓研究者可以使用腦電波圖記錄分析這些神經活動。由於大腦在本質上是一個非線性、非穩態的動態系統，而傳統的神經動力學分析法皆預設資料是非穩態或者至少是線性的，我們實際上只對整個腦部歷程作小部分且極不完整的描述。為突破傳統研究取向的不足，本計畫將採用全息頻譜分析法（Holo-Hilbert Spectral Analysis, HHSA）以研究大腦視覺系統的非線性特質。藉由探究不同頻率與波型變化的閃爍刺激所產生的穩態視覺誘發電位（steady-state visual evoked potential, SSVEP），探討視覺系統的非線性處理特質與其機制。同時利用調幅視覺刺激以估計視覺訊息在系統中的傳播速度與擴散方式。更進一步以前述的實驗結果為基礎，探討視覺經驗例如雙眼拮抗（binocular rivalry）及運動後效（motion aftereffect）和刺激呈現頻率與波型變化的交互作用。以及閃爍視覺刺激作為以後非侵入性腦部刺激技術作為評估及改善認知功能的潛在可能性，最終的目標是藉由釐清視覺系統的非線性特徵與處理機制，以窺視整個腦部神經活動的非線性運作方式並建立相對應的理論。 ;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.