| 摘要: | 許多脊髓損傷患者或是其他類型患者像是肌萎縮性側索硬化症、腦幹中風、大腦或脊髓受傷、腦性麻痺、肌肉失養症、多發性硬化症等病患。這些患者無法與外界溝通或是自由的移動，為了改善這些行動有困難的人，使他們能夠自由移動，有種使用腦部直接控制電腦機械，不用透過肌肉來控制的一個溝通與控制管道，在此稱為大腦人機介面(Brain Computer interface, BCI)，透過這個大腦人機介面，就可以讓這些脊髓損傷患者與部世界傳遞訊息以及傳遞控制命令。 本研究提出一個利用穩態視覺誘發電位達成的大腦人機介面系統，使用黃鍔所提出的整體經驗模態分解法(Ensemble Empirical Mode Decomposition, EEMD)去除基線漂移和其它雜訊，並利用Quadrature Detection來判斷SSVEP之頻率，並將此方法在LabVIEW平台上完成，之後使用433Mhz無線傳輸模組傳送控制訊號給遙控車，達到即時控制遙控車之BCI。 Patients with spinal cord injury or neuromuscular disorders, such as Amyotrophic lateral sclerosis (ALS), brainstem stroke, brain or spinal cord injury, cerebral palsy, muscular dystrophies, multiple sclerosis, and etc, can not communicate with external environments. In order to solve this problem, researchers are engaging themselves in developing new techniques, which are independent of their peripheral neuromuscular functions, to help them express their intentions. One plausible way, the brain–computer interface (BCI), has drawn great attention and regarded as a potential technique. This study adopts ensemble empirical mode decomposition (EEMD) to implement a fast steady-state visual evoked potential (SSVEP) – based BCI system. Taking the advantage of EEMD for noise suppression in pre-processing step, SSVEPs can be extracted with high signal-to-noise ratio (SNR) and it permits some phase detection technique, such as quadrature detection (QD), can be applied to estimate the existing frequency of SSVEP in a short-time data segment. The proposed system has successfully implemented to control a remote-controlled car with acceptable accuracy and high information transfer rate (ITR). |
