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結合CSP 和HMM 的左右手運動想象分類
來源: | 作者: | 發布時間:2014-4-15 18:14:18

 結合CSP 和HMM 的左右手運動想象分類
宋俊可,吳小培
基金項目:博士點基金(200803570002);國家自然科學基金資助項目(60771033)
作者簡介:宋俊可,(1988-),女,碩士研究生,生物醫學信號處理.
(安徽大學,計算智能與信號處理教育部重點實驗室,合肥 230039)
摘要:腦機接口是不依賴于動作和語言,在大腦與計算機或其它電子設備之間建立直接的交流和控制通道,讓人可以通過大腦來直接的表達想法和操縱設備。對于以運動想象為基礎的腦機接口系統,文中把2003BCI 標準數據和實驗室實測數據作為處理對象,將數據經過分段帶通濾波預處理、共同空間模式(CSP)空間濾波提取特征向量,最后用隱馬爾科夫模型(HMM)進行分類識別。實驗結果表明,該方法是一種有效的運動想象分類方法。
關鍵詞:腦機接口;運動想象;共同空間模式;隱馬爾科夫模型
中圖分類號:R318
Classification of Right and Left Hand Motor Imagery Based on CSP Algorithm and HMM
SONG Junke, WU Xiaopei
(The Key Laboratory of Intelligent Computing & Signal Processing Anhui University,HeFei 230039)
Abstract: Brain-Computer Interface(BCI)enables the users to express their ideas and manipulate devices through their brains directly by creating an interaction and control channel between human brain and computers or other electronic equipments without any language speaking or body movement. In this paper, based on a motion imagination BCI system, CSP
spatial filtering is adopted to extract features after a band-pass filtering as a preprocessing is performed. The classification is finally achieved using HMM. The experiment results on both the 2003 BCI standard data and the real-life data collected by our laboratory demonstrate that the proposed algorithm works effectively in motor imagery classification.
Keywords:Brain-Computer Interface(BCI); motor imaery; Common Spatial Pattern(CSP); Hidden Markov Model(HMM)

 

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