本研究主要利用圖形辨識及類神經網路兩大技術來分析、解讀、與學習腦波訊號，進而判讀受測者的想法。研究先以高精度腦波儀 OpenBCI進行量測，擷取八個波段的生理腦波電訊號，接著以Google的開源API－Teachable Machine來訓練系統，經學習後不僅可以判斷受測者的精神狀態為專注亦或是放鬆，此外還能分辨受測者是在想左邊還右邊。此研究成果在腦波的判讀技術上可視為一重大發展。;This study based on two major technologies: Artificial Neural netw-ork and pattern recognition. By using these technologies, we can analyze, interpret, and learn brainwave signals; furthermore, interpret the subject′s thoughts. At first, the study measured with a high-precision electroencep-halogram OpenBCI and captured eight wavebands of physiological brain-wave signals. Then we use Google′s open source API-Teachable Machine to train the system recognizing brainwave pattern. After learning, it can n-ot only distinguish between focused and relaxed from the subject′s mental state, but also distinguish between left and right from the subject′s thinki-ng. This research result can be regarded as a major development in the in-terpretation of brain science.
