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題 名 | Classifying MEG 20Hz Rhythmic Signals of Left, Right Index Finger Movement and Resting State Using Cascaded Radial Basis Function Networks=利用串聯的幅射基底類神經網路進行不同狀態下20Hz腦磁波的分類 |
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作 者 | 李柏磊; 陳麗芬; 吳育德; 邱秀鳳; 徐國鎧; 葉子成; 謝仁俊; | 書刊名 | Journal of Medical and Biological Engineering |
卷 期 | 22:3 2002.09[民91.09] |
頁 次 | 頁147-152 |
分類號 | 410.1644 |
關鍵詞 | 幅射基底類神經網路; β頻帶腦波律動訊號; 腦機介面系統; Radial basis neural network; Beta-band brain rhythm; Brain computer interface; |
語 文 | 英文(English) |
中文摘要 | 我們量測四位受試者的運動區β頻帶腦磁波律動訊號(β band sensorimotor beta rhythm),並使用串聯的幅射基底類神經網路對受試者的左右手食指上抬運動與休息狀態進行分類。在本研究中,此串聯的幅射基底類神經網路共分為兩個網路層級,第一級的幅射基底類神經網路是用來判斷受試者是處於休息狀況還是手指運動的狀況下,第二級的幅射基底類神經訊號是用來判斷受試者到底是左手運動還是右手運動。運用此串聯的幅射基底類神經網路,對四位受試者的左右手食指上抬運動與休息狀態時的辨識率(休息,左手動,右手動)可分別達到(100, 91, 66)%、(100, 80, 72)%、(100, 76, 73)%與(100, 74, 94)%。 |
英文摘要 | A cascaded Radial-Basis Functions (RBF) network was devised to classify the magnetoencephalography (MEG) rhythmic signals of the left, right index finger movement and resting state. Four right-handed subjects were instructed to perform self-paced index finger lifting in a rate of every 8 sec. MEG epochs from 4000 ms pre-movement to 3000 ms post-movement were digitized. Each trial was decomposed by Principal Component Analysis (PCA) and the task-related components were selected to reconstruct data followed by band-passed filtering around 16-20 Hz. Every five filtered epochs were averaged and processed by the Hilbert transform to produce a beta-band envelope. All the envelopes around sensorimotor channels were normalized and down-sampled to 100 samples. A movement feature vector was defined by concatenating two mean envelopes in the left and right channels. We defined a resting feature vector by averaging five envelopes randomly selected in the left and right channels respectively during the resting state, and then concatenating two means. Two cascaded 3-layer RBF networks were constructed in which the first RBF network was to discriminate the movement from the resting state, while the second one was to distinguish the left and right index finger movement. The classification rates (rest, left, right) for four subjects achieve (100, 91, 66)%, (100, 80, 72)%, (100, 76, 73)% and, (100, 74, 94)%, respectively. |
本系統中英文摘要資訊取自各篇刊載內容。