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基于小波能量矩的嗅觉脑电信号识别

翟文鹏 张小内 侯惠让 孟庆浩

翟文鹏, 张小内, 侯惠让, 孟庆浩. 基于小波能量矩的嗅觉脑电信号识别[J]. 仁和测试, 2020, 37(3): 399-404. doi: 10.7507/1001-5515.201910036
引用本文: 翟文鹏, 张小内, 侯惠让, 孟庆浩. 基于小波能量矩的嗅觉脑电信号识别[J]. 仁和测试, 2020, 37(3): 399-404. doi: 10.7507/1001-5515.201910036
Wenpeng ZHAI, Xiaonei ZHANG, Huirang HOU, Qinghao MENG. Olfactory electroencephalogram signal recognition based on wavelet energy moment[J]. Rhhz Test, 2020, 37(3): 399-404. doi: 10.7507/1001-5515.201910036
Citation: Wenpeng ZHAI, Xiaonei ZHANG, Huirang HOU, Qinghao MENG. Olfactory electroencephalogram signal recognition based on wavelet energy moment[J]. Rhhz Test, 2020, 37(3): 399-404. doi: 10.7507/1001-5515.201910036

基于小波能量矩的嗅觉脑电信号识别

doi: 10.7507/1001-5515.201910036
基金项目: 国家自然科学基金资助项目(61573253)
详细信息
    通讯作者:

    孟庆浩,Email:qh_meng@tju.edu.cn

Olfactory electroencephalogram signal recognition based on wavelet energy moment

Funds: National Natural Science Foundation of China
More Information
  • 摘要: 研究大脑对不同气味的识别能力在嗅觉功能障碍评估和诊断等方面具有重要意义。本文提出将小波能量矩(WEM)作为嗅觉诱发脑电图(EEG)信号特征并用于气味分类。首先,通过试验采集 13 种气味的嗅觉诱发 EEG 数据;其次,从嗅觉诱发 EEG 数据中提取 WEM 作为信号特征,并将功率谱密度(PSD)、近似熵、样本熵及小波熵作为对比特征;最后,利用 k 近邻(k-NN)、支持向量机(SVM)、随机森林(RF)和决策树分类器识别不同的气味。结果表明,使用以上 4 种分类器,WEM 特征分类准确率均高于其它特征,其中 k-NN 分类器与 WEM 特征结合的分类准确率最高(91.07%)。本文进一步对不同 EEG 信号的频带进行了探究,发现大多数基于 γ 频带的分类准确率优于全频带及其他频带,其中 γ 频带 WEM 特征结合 k-NN 分类器的分类准确率最高(93.89%)。本文的研究结果一方面可为嗅觉功能评价提供新的客观依据,另一方面,也可为嗅觉诱发情绪的研究提供新的思路。
  • 图  1  嗅觉脑电数据采集试验

    Figure  1.  Experiment of olfactory EEG data acquisition

    图  2  13 种气味分别进行 35 次测试的嗅觉诱发 EEG 信号试验流程图

    Figure  2.  Flowchart of olfactory EEG experiment: 35 tests for each of thirteen scents

    图  3  采样频率为 128 Hz 的 EEG 信号 4 层小波分解

    Figure  3.  4-layer wavelet decomposition of EEG signals with a sampling rate of 128 Hz

    图  4  采用不同特征和不同分类器对 13 种气味的分类结果

    Figure  4.  Recognition results of 13 scents with different features and classifiers

    表  1  不同频带的分类准确率(%)

    Table  1.   Classification accuracy of different frequency bands (%)

    分类器 特征 全频带 θ α β γ
    k-NN WEM 91.07 36.17 68.83 66.69 93.89
    PSD 83.76 33.28 36.15 58.55 89.07
    近似熵 69.74 29.95 50.94 60.21 68.65
    样本熵 65.16 26.78 48.15 57.54 66.08
    小波熵 55.47 55.79 47.50 56.73 57.63
    SVM WEM 84.98 21.91 77.85 77.48 93.52
    PSD 79.02 20.51 25.04 64.43 92.61
    近似熵 67.51 22.66 49.27 57.86 70.72
    样本熵 64.71 24.34 46.67 57.76 68.90
    小波熵 56.41 52.72 37.06 58.83 53.19
    决策树 WEM 84.43 30.62 59.42 59.71 86.90
    PSD 79.83 27.47 30.54 53.95 81.85
    近似熵 57.17 24.38 42.79 47.44 56.07
    样本熵 55.79 23.32 40.41 45.60 54.82
    小波熵 47.94 46.22 36.43 42.95 43.28
    RF WEM 88.38 35.31 65.76 66.43 91.06
    PSD 85.83 31.60 35.51 62.41 86.55
    近似熵 70.54 28.60 50.33 58.20 64.14
    样本熵 67.29 28.24 47.80 55.67 61.97
    小波熵 53.65 51.27 41.72 49.82 48.54
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-10-18
  • 修回日期:  2020-04-27
  • 刊出日期:  2020-03-17

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