| DC 欄位 |
值 |
語言 |
| DC.contributor | 照明與顯示科技研究所 | zh_TW |
| DC.creator | 劉景浩 | zh_TW |
| DC.creator | Ching-Hao Liu | en_US |
| dc.date.accessioned | 2018-7-23T07:39:07Z | |
| dc.date.available | 2018-7-23T07:39:07Z | |
| dc.date.issued | 2018 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=105232003 | |
| dc.contributor.department | 照明與顯示科技研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 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。 | zh_TW |
| dc.description.abstract | 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. | en_US |
| DC.subject | 腦波 | zh_TW |
| DC.subject | 大腦科學 | zh_TW |
| DC.subject | 類神經網路 | zh_TW |
| DC.subject | 圖形辨識 | zh_TW |
| DC.title | 類神經網路暨圖形辨識之腦波判讀系統 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | The Interpretation System of the Brain Wave(EEG) by Artificial Neural Network and Pattern Recognition | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |