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系統識別號 U0026-0812200911163623
論文名稱(中文) 應用於指動偵測之腦波訊號分析系統
論文名稱(英文) EEG Signal Analysis System for Finger Movement Detection
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 92
學期 2
出版年 93
研究生(中文) 劉勇均
研究生(英文) Yung-Chun Liu
電子信箱 linkeyliu@yahoo.com.tw
學號 P7691414
學位類別 碩士
語文別 中文
論文頁數 83頁
口試委員 指導教授-孫永年
口試委員-朱銘祥
口試委員-林宙晴
口試委員-李建樹
口試委員-吳育德
口試委員-張燕光
中文關鍵字 腦波訊號分析  人腦電腦介面  即時分析  有效時間區段選取法 
英文關鍵字 BCI  real-time  brain-computer interface  active time segment selection  EEG 
學科別分類
中文摘要   許多神經疾病,例如中風和脊髓損傷,會阻斷大腦皮質和肌肉之間的聯繫。另外,有一些疾病會破壞肌肉本身。而這些疾病都會對個體的自主運動造成妨礙,進而影響到個體的工作能力。人腦-電腦介面(Brain- computer interface, BCI)藉由創造新的大腦輸出路徑,使這些患者能自行與周遭事物進行互動,來改善上述的缺陷。
  腦波訊號分析是人腦-電腦介面中最普遍用來產生控制訊號源的方法。而指動辨識是此領域的研究重點之一。但過去在這方面的研究中,腦波試驗的長度大多介於4~10秒,因此若要將之應用於即時分析則有其困難度。有鑑於此,本論文以研究一秒鐘長度的腦波分析技術為基礎,來發展一套可用於即時偵測指動的腦波訊號分析系統。我們以有效時間區段選取法,選取出腦波試驗中最適合用來辨識指動事件的時間區段,並以所有試驗中此區段的資訊來訓練分類器,再配合一階層式分析架構來達成即時偵測指動的構想。另外,我們透過自動化的方式來對系統各階層的辨識結果作統計分析。
  經由實驗結果顯示,我們的系統已能由連續的腦波訊號序列中成功地辨識出手指擺動與否,並進一步辨識出哪一手動,未來我們預計將之運用在臨床醫療輔具的控制上,以造福神經疾病患者或肢體殘缺人士。
英文摘要   Many neurological diseases, such as stroke and spinal cord injury, disrupt the connections between brain cortex and muscles. Besides, some other diseases may destruct the muscle and make it functionless. All these diseases interfere with the voluntary movements of the subjects and influence their ability to accomplish the attempted task. Brain-computer interface (BCI), which defines an artificial alternative output from the brain cortex to make communication with their surrounding targets, can improve above deficits.
  The most common way of BCI is to give control signals based on the analysis of Electroencephalogram (EEG) signals. And the recognition of finger movements has been one of the most important issues in this field. In the previous researches, the length of EEG trials for analysis were usually between 4 to 10 seconds, therefore it would have difficulties in real-time applications. For this reason, we study the technique of analyzing the EEG signals which have the length of one second, and construct a real-time EEG recognition system based on it for detecting finger movements. We adopt the strategy, named Active Time Segment Selection, to pick the most appropriate time segment of the EEG trial for the recognition of finger movements. And the classifier is trained with the information of this segment in all trials. The integrated processes with the above-mentioned functions form a two-staged recognition system to classify the finger motions in real-time. Besides, we propose an automatic approach to provide statistical analysis on the results of recognition in each stage.
  From the results of the experiment, it has shown that our system can distinguish a finger movement or a non-movement from the input EEG signal sequences, and further recognize the movement as a left or a right one successfully. We expect to use the system in controlling clinical assistive devices in the future, and benefit the subjects with neurological diseases or limb disabilities.
論文目次 目錄

第一章 緒論 1
1.1 研究背景 1
1.2 感覺運動皮質區(sensorimotor cortex)所產生之μ波與β波 4
1.3 研究動機與目的 7
1.4 論文章節概述 8

第二章 資料蒐集與前處理 9
2.1 資料蒐集 9
2.1.1 實驗相關資訊 9
2.1.2 實驗步驟 13
2.2 前處理 14
2.2.1 資料切割 14
2.2.2 Laplacian運算 16

第三章 指動偵測之腦波訊號分析 18
3.1特徵擷取 18
3.1.1 特徵類別 19
3.1.2 特徵向量 23
3.1.3 有效時間區段選取法(Active Time Segment Selection) 28
3.2 分類辨識 33
3.2.1分類方法 33
3.2.2離線之事件分類 38
3.2.3階層式即時分析架構 38
3.3 即時系統分類結果之統計分析 43
3.3.1 Confusion matrix 43
3.3.2 系統分類結果之自動統計機制 44

第四章 實驗結果與討論 51
4.1 有效時間區段之選取 51
4.2 離線之事件分類 62
4.3 即時分析之模擬 69

第五章 結論與未來展望 77
5.1 結論 77
5.2 未來展望 78

參考文獻 79
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