【摘要】：通過提取單電極中潛伏期反應(MLR)的特征差異,研究并實現(xiàn)了正常個體聽覺注意與非注意2種狀態(tài)的識別.首先,對MLR信號進行小波濾波、閾值去偽跡、相干平均等預處理;然后,分析了MLR在2種狀態(tài)下的成分波差異,并將Na,Pa,Nb波的幅值與能量、面積、C0復雜度、AR模型系數(shù)等傳統(tǒng)特征組合成為新的特征向量;最后,采用支持向量機(SVM)和人工神經(jīng)網(wǎng)絡(ANN)在傳統(tǒng)特征向量和新特征向量下進行目標識別.8位被試的實驗結果顯示,在2種不同狀態(tài)下,被試的Na,Pa,Nb波幅值具有顯著性差異(p0.05),而潛伏期并無差異.ANN作為分類器時,新特征向量的平均識別正確率可達85.7%.由此可見,利用單電極中潛伏期反應區(qū)分聽覺注意與非注意狀態(tài)是有效的.
[Abstract]:By extracting the characteristic difference of latency response (MLR) in single electrode, the recognition of two states of auditory attention and non-attention in normal individuals was studied and realized. Firstly, the MLR signal is preprocessed with wavelet filtering, threshold de-artifact, coherent average, etc. Then, the difference of component wave in two states of MLR is analyzed, and the amplitude and energy of Na,Pa,Nb wave are analyzed. The traditional features such as area C0 complexity and AR model coefficients are combined into new feature vectors. Finally, Support vector machine (SVM) and artificial neural network (ANN) are used to recognize the target under traditional and new feature vectors. The experimental results show that in two different states, The amplitude of Na,Pa,Nb was significantly different (p0. 05), but there was no difference in latency. Ann was used as classifier, the average recognition accuracy of the new feature vector could reach 85.7%. Therefore, it is effective to distinguish auditory attention from non-attention by single-electrode mid-latency response.
【作者單位】： 東南大學信息科學與工程學院;廣州大學機械與電氣工程學院;南京工程學院通信工程學院;
【基金】：國家自然科學基金資助項目(61375028,61673108) 江蘇省“六大人才高峰”資助項目(2016-DZXX-023) 江蘇省博士后科研資助計劃資助項目(1601011B) 江蘇省“青藍工程”資助項目 廣州大學廣東省燈光與聲視頻工程技術研究中心開放基金資助項目(KF201601,KF201602)
【分類號】：R318;TP18
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本文編號：2210122