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研究生:陳煒燁
研究生(外文):Wei-Ye Chen
論文名稱:利用SVM分類演算法於EEG-P300的偵測與分析
論文名稱(外文):DETECTION AND RECOGNITION OF EEG-P300 USING SUPPORT VECTOR MACHINES
指導教授:張寧群張寧群引用關係
指導教授(外文):Ning-Cyun Chang
學位類別:碩士
校院名稱:逢甲大學
系所名稱:自動控制工程所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:59
中文關鍵詞:支持向量機P300腦波事件相關電位
外文關鍵詞:ElectroencephalogramEvent-Related PotentialsP300Support Vector Machine
相關次數:
  • 被引用被引用:7
  • 點閱點閱:847
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:2
許多意外或生理疾病,會阻斷大腦與肌肉之間的聯繫,造成病患自主活動能力上的不便,大腦人機介面-BCI,旨在提供此類嚴重肢障患者得以意念操控電腦或其他輔助器材,然而腦波信號既微弱且複雜,如何以特定事件來誘發,並適時取得有意義的信號是發展BCI系統的重要關鍵。本研究主要擷取腦波中事件相關電位的P300誘發電位,以支持向量機分類演算法來切入,設計一套結合視覺與意念的事件來偵測P300。實驗結果顯示,我們成功的誘發並辨識P300誘發電位出現的有無,並探討其疊加次數與準確率的關係。希望此研究成果,可以幫助大腦人機介面的開發,造福更多因疾病或傷害而肢體行動不變的傷患。
Some diseases will break the interconnection between brain and muscles then result in the inconvenience of patient’s daily life. Brain-computer Interface is to provide an access for those seriously handicapped to control computer or other assisting apparatus by their own will. However, EEG signals are weak and complicated, and how to induce by specific events and capture signal at the right moment will be the key factor in developing BCI system. This research is to capture event-related P300 Potentials and analyze with support vector machine, then a system combined with vision and consciousness events for P300 Potentials is designed. The experimental result showed that P300 potentials are successfully induced and recognized, and the relation between iterative number and accuracy is also discussed. We deeply hope the result of this research will be a great help in developing BCI system and benefit more handicapped patient suffered from diseases or accidents.
中文摘要 ii
Abstract iii
誌謝 iv
目錄 v
圖目錄 vii
表目錄 viii
第一章 緒論 1
1.1 前言 1
1.2 研究動機 2
1.3 研究目的 3
1.4 文獻回顧 5
第二章 研究背景探討 7
2.1 大腦生理結構 7
2.2 腦波簡介 11
2.2.1 腦波的源起與量測 11
2.2.2 腦波的性質與腦波圖 15
2.3誘發電位 17
2.4 Oddball Paradigm 20
第三章 研究方法與流程架構 22
3.1 實驗硬體及方法設計 23
3.1.1腦波擷取系統 23
3.1.2 視覺刺激面板 24
3.1.3 實驗步驟 27
3.1.4 訊號前處理 28
3.2支持向量機 30
3.2.1 基礎理論 30
3.2.2 公式推導 32
3.2.3 Kernel 35
3.2.4 Soft Margin 37
3.2.5 Libsvm 38
第四章 實驗結果與討論 40
4.1P300誘發電位波形 40
4.2辨識結果 43
4.3準確率統計分析 45
第五章 結論與未來展望 46
5.1結論 46
5.2未來展望 47
參考文獻 48
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