高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于条件经验模式分解和串并行CNN的脑电信号识别

唐贤伦 李伟 马伟昌 孔德松 马艺玮

唐贤伦, 李伟, 马伟昌, 孔德松, 马艺玮. 基于条件经验模式分解和串并行CNN的脑电信号识别[J]. 电子与信息学报, 2020, 42(4): 1041-1048. doi: 10.11999/JEIT190124
引用本文: 唐贤伦, 李伟, 马伟昌, 孔德松, 马艺玮. 基于条件经验模式分解和串并行CNN的脑电信号识别[J]. 电子与信息学报, 2020, 42(4): 1041-1048. doi: 10.11999/JEIT190124
Xianlun TANG, Wei LI, Weichang MA, Desong KONG, Yiwei MA. Conditional Empirical Mode Decomposition and Serial Parallel CNN for ElectroEncephaloGram Signal Recognition[J]. Journal of Electronics and Information Technology, 2020, 42(4): 1041-1048. doi: 10.11999/JEIT190124
Citation: Xianlun TANG, Wei LI, Weichang MA, Desong KONG, Yiwei MA. Conditional Empirical Mode Decomposition and Serial Parallel CNN for ElectroEncephaloGram Signal Recognition[J]. Journal of Electronics and Information Technology, 2020, 42(4): 1041-1048. doi: 10.11999/JEIT190124

基于条件经验模式分解和串并行CNN的脑电信号识别

doi: 10.11999/JEIT190124
基金项目: 国家自然科学基金(61673079, 61703068),重庆市基础研究与前沿探索项目(cstc2018jcyjAX0160)
详细信息
    作者简介:

    唐贤伦:男,1977年生,教授,博士,研究方向为智能系统与机器人,模式识别理论与应用

    李伟:男,1996年生,硕士生,研究方向为深度学习、脑电信号识别

    马伟昌:男,1995年生,硕士生,研究方向为机器人控制

    孔德松:男,1993年生,硕士生,研究方向为深度学习

    马艺玮:女,1980年生,副教授,博士,研究方向为智能信息处理

    通讯作者:

    李伟 cqyddxliwei@foxmail.com

  • 中图分类号: TP391.4

Conditional Empirical Mode Decomposition and Serial Parallel CNN for ElectroEncephaloGram Signal Recognition

Funds: The National Natural Science Foundation of China (61673079, 61703068), The Basic Research and Frontier Exploration Project of Chongqing (cstc2018jcyjAX0160)
  • 摘要:

    针对运动想象脑电信号(EEG)的非线性、非平稳特点,该文提出一种结合条件经验模式分解(CEMD)和串并行卷积神经网络(SPCNN)的脑电信号识别方法。在CEMD过程中,采用各阶固有模式分量(IMF)与原始信号的相关性系数作为第1个IMF筛选条件,在此基础上,提出各阶IMF之间的相对能量占有率作为第2个IMF筛选条件。此外,为了考虑脑电信号各个通道之间的特征和突出每个通道内的特征,该文提出SPCNN网络模型对进行CEMD过程后的脑电信号进行分类。实验结果表明,在自行采集的脑电数据集上平均识别率达到94.58%。在公开数据集BCI competition IV 2b上平均识别率达到82.13%,比卷积神经网络提高了3.85%。最后,在自行设计的智能轮椅脑电控制平台上进行了轮椅前进、左转和右转在线控制实验,验证了该文算法对脑电信号识别的有效性。

  • 图  1  串并行卷积神经网络结构图

    图  2  Emotiv脑电采集仪

    图  3  Emotiv脑电采集仪电极安放位置

    图  4  单次脑电信号采集过程

    图  5  设定不同的阈值$\alpha $时识别率情况

    图  6  设定不同的阈值$\beta $时识别率情况

    图  7  采用不同处理方法的识别准确率对比

    图  8  智能轮椅系统结构图

    表  1  不同算法对5受试者脑电信号的识别准确率(%)

    算法CSPACSPDBNCNNSTFT-CNNSPCNN本文CEMD-SPCNN
    S0165.0077.5087.0886.2588.7590.4293.33
    S0281.6782.9287.5087.9289.1791.2594.17
    S0398.3397.0895.8395.8396.6797.0899.16
    S0476.2578.3383.3385.4285.4286.2589.58
    S0595.4296.2593.7591.6792.5094.1796.67
    均值83.3386.4189.5089.4290.5091.8394.58
    方差190.0191.8826.5118.6118.1816.6213.02
    下载: 导出CSV

    表  2  不同算法对BCI competition IV 2b数据集的识别准确率(%)

    算法ChinGanCoyleCSPACSPDBNCNNSTFT-CNNSPCNN本文CEMD-SPCNN
    B0170.0071.0060.0066.5667.5066.5672.2275.0076.3980.56
    B0261.0061.0056.0057.8155.3162.5061.0361.7663.2464.71
    B0361.0057.0056.0061.2562.1960.0061.1162.5062.5064.58
    B0498.0097.0089.0094.0694.6996.8798.6598.6599.3299.32
    B0593.0086.0079.0080.6376.8882.1986.4887.1687.8488.51
    B0681.0081.0075.0075.0075.9477.5079.1780.5681.2583.33
    B0778.0081.0069.0072.5071.2576.5678.4777.0879.1781.25
    B0893.0092.0093.0089.3889.3888.7586.1886.1886.8490.13
    B0987.0089.0073.0085.6381.2585.9481.2582.6484.0386.81
    均值80.2279.4472.2275.8674.9377.4378.2879.0680.0682.13
    方差192.19190.03181.69158.75157.50155.85147.92138.63137.52129.78
    下载: 导出CSV

    表  3  各类操作在线识别准确率(%)

    操作直行左转右转
    S01968486
    S02949082
    S03988892
    下载: 导出CSV
  • RAMADAN R A and VASILAKOS A V. Brain computer interface: Control signals review[J]. Neurocomputing, 2017, 223: 26–44. doi: 10.1016/j.neucom.2016.10.024
    佘青山, 陈希豪, 高发荣, 等. 基于感兴趣脑区LASSO-Granger因果关系的脑电特征提取算法[J]. 电子与信息学报, 2016, 38(5): 1266–1270. doi: 10.11999/JEIT150851

    SHE Qingshan, CHEN Xihao, GAO Farong, et al. Feature extraction of electroencephalography based on LASSO-Granger causality between brain region of interest[J]. Journal of Electronics &Information Technology, 2016, 38(5): 1266–1270. doi: 10.11999/JEIT150851
    YU Tianyou, XIAO Jun, WANG Fangyi, et al. Enhanced motor imagery training using a hybrid BCI with feedback[J]. IEEE Transactions on Biomedical Engineering, 2015, 62(7): 1706–1717. doi: 10.1109/TBME.2015.2402283
    KAUFMANN T, HERWEG A, and KÜBLER A. Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials[J]. Journal of NeuroEngineering and Rehabilitation, 2014, 11: No.7. doi: 10.1186/1743-0003-11-7
    WANG Haofei, DONG Xujiong, CHEN Zhaokang, et al. Hybrid gaze/EEG brain computer interface for robot arm control on a pick and place task[C]. The 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, Italy, 2015: 1476–1479. doi: 10.1109/EMBC.2015.7318649.
    陈强, 陈勋, 余凤琼. 基于独立向量分析的脑电信号中肌电伪迹的去除方法[J]. 电子与信息学报, 2016, 38(11): 2840–2847. doi: 10.11999/JEIT160209

    CHEN Qiang, CHEN Xun, and YU Fengqiong. Removal of muscle artifact from EEG data based on independent vector analysis[J]. Journal of Electronics &Information Technology, 2016, 38(11): 2840–2847. doi: 10.11999/JEIT160209
    HSU W Y. EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features[J]. Journal of Neuroscience Methods, 2010, 189(2): 295–302. doi: 10.1016/j.jneumeth.2010.03.030
    WU Shanglin, WU Chunwei, PAL N R, et al. Common spatial pattern and linear discriminant analysis for motor imagery classification[C]. 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Singapore, 2013: 146–151. doi: 10.1109/CCMB.2013.6609178.
    ZHANG Yu, WANG Yu, ZHOU Guoxu, et al. Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces[J]. Expert Systems with Applications, 2018, 96: 302–310. doi: 10.1016/j.eswa.2017.12.015
    KEVRIC J and SUBASI A. Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system[J]. Biomedical Signal Processing and Control, 2017, 31: 398–406. doi: 10.1016/j.bspc.2016.09.007
    PARK C, LOONEY D, REHMAN N U, et al. Classification of motor imagery BCI using multivariate empirical mode decomposition[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(1): 10–22. doi: 10.1109/TNSRE.2012.2229296
    DOSE H, MØLLER J S, IVERSEN H K, et al. An end-to-end deep learning approach to MI-EEG signal classification for BCIs[J]. Expert Systems with Applications, 2018, 114: 532–542. doi: 10.1016/j.eswa.2018.08.031
    ZHANG Jin, YAN Chungang, and GONG Xiaoliang. Deep convolutional neural network for decoding motor imagery based brain computer interface[C]. 2017 IEEE International Conference on Signal Processing, Communications and Computing, Xiamen, China, 2017: 1–5. doi: 10.1109/ICSPCC.2017.8242581.
    AN Xiu, KUANG Deping, GUO Xiaojiao, et al. A deep learning method for classification of EEG data based on motor imagery[C]. The 10th International Conference on Intelligent Computing, Taiyuan, China, 2014: 203–210. doi: 10.1007/978-3-319-09330-7_25.
    LI Shufang, ZHOU Weidong, YUAN Qi, et al. Feature extraction and recognition of ictal EEG using EMD and SVM[J]. Computers in Biology and Medicine, 2013, 43(7): 807–816. doi: 10.1016/j.compbiomed.2013.04.002
    TARAN S, BAJAJ V, SHARMA D, et al. Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications[J]. Measurement, 2018, 116: 68–76. doi: 10.1016/j.measurement.2017.10.067
    MU Yashuang, LIU Xiaodong, and WANG Lidong. A Pearson’s correlation coefficient based decision tree and its parallel implementation[J]. Information Sciences, 2018, 435: 40–58. doi: 10.1016/j.ins.2017.12.059
    LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278–2324. doi: 10.1109/5.726791
    SAKHAVI S, GUAN Cuntai, and YAN Shuicheng. Learning temporal information for brain-computer interface using convolutional neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(11): 5619–5629. doi: 10.1109/TNNLS.2018.2789927
    SUN Shiliang and ZHOU Jin. A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces[C]. 2014 International Joint Conference on Neural Networks, Beijing, China, 2014: 1746–1753. doi: 10.1109/IJCNN.2014.6889525.
    LU Na, LI Tengfei, REN Xiaodong, et al. A deep learning scheme for motor imagery classification based on restricted Boltzmann machines[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(6): 566–576. doi: 10.1109/TNSRE.2016.2601240
  • 加载中
图(8) / 表(3)
计量
  • 文章访问数:  806
  • HTML全文浏览量:  689
  • PDF下载量:  50
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-03-01
  • 修回日期:  2019-11-22
  • 网络出版日期:  2019-12-14
  • 刊出日期:  2020-06-04

目录

    /

    返回文章
    返回

    官方微信,欢迎关注