Study of objective visual EEG mode classification based on Hilbert-Huang transform

doi:10.3778/j.issn.1002-8331.2009.21.061

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (21): 212-214.DOI: 10.3778/j.issn.1002-8331.2009.21.061

• 工程与应用 • Previous Articles Next Articles

Study of objective visual EEG mode classification based on Hilbert-Huang transform

ZHOU Li-xia¹，FAN Ying-le¹，LI Yi ^1，2

1.Institute of Biomedical Engineering and Instrument，Hangzhou Dianzi University，Hangzhou 310018，China
2.Institute of Biomedical Engineering and Instrument，Zhejiang University，Hangzhou 310027，China

Received:2008-04-21 Revised:2008-07-11 Online:2009-07-21 Published:2009-07-21
Contact: ZHOU Li-xia

基于Hilbert-Huang变换的目标注视脑电模式分类研究

周丽霞¹，范影乐¹，李轶^1，2

1.杭州电子科技大学生物医学工程与仪器研究所，杭州 310018
2.浙江大学生物医学工程与仪器学院，杭州 310027

通讯作者: 周丽霞

Abstract

Abstract: A research of automatic classification of visual objective is made.A new application of Hilbert-Huang transform method to a character matrix（6 by 6） is presented which is scanned repeadly.With the EMD method，the original signal is decomposed into several intrisic mode functions.With the Hilbert transform，energy-frequency distribution is computed to decide which character is imaged.Finally the pattern recognition method of nearest neighbors is applied to optimal classification.The experiment data from 3rd BCI competition is analyzed，and classification is made.The mean classification accuracy is up to 84.21%.Based on the Hilbert-Huang transform of EEG signals，the classification of character which is imaged can be made effectively，which will help to develop the BCI system.

Key words: Hilbert-Huang transform, Electro Encephalogram（EEG）, P300, intrinsic mode function, event-relative

摘要： 研究目标注视任务，即类“思维拨号”任务下的脑电信号模式自动分类技术。基于时频分析原理，对循环扫视6×6字符矩阵所得的脑电信号进行Hilbert-Huang变换，用EMD（Empirical Mode Decomposition）方法把信号分解成数个本征模态函数（Intrinsic Mode Function，IMF）；对每个IMF进行Hilbert变换，得到时频平面上的能量分布，即Hilbert谱；将各频率分量累加谱按时间变化特性作为关注与非关注某字符的特征量；最后通过K-近邻法对所视字符进行自动识别。对第3届国际脑机接口竞赛的数据进行分析测试，识别准确率达到84.21%。经Hilbert-Huang变换的时频分析方法，可以得到目标注视任务下的脑电信号有效特征，从而有助于脑机接口系统的实际应用。

关键词: Hilbert-Huang变换, 脑电信号, P300, 固有模态函数, 事件相关

ZHOU Li-xia¹，FAN Ying-le¹，LI Yi ^1，2. Study of objective visual EEG mode classification based on Hilbert-Huang transform[J]. Computer Engineering and Applications, 2009, 45(21): 212-214.

周丽霞¹，范影乐¹，李轶^1，2. 基于Hilbert-Huang变换的目标注视脑电模式分类研究
[J]. 计算机工程与应用, 2009, 45(21): 212-214.

[1]	YAN Hongwen, LU Geyu. Application research on complete ensemble empirical mode decomposition, wavelet transform and convolutional neural networks in short-term wind speed forecasting [J]. Computer Engineering and Applications, 2018, 54(9): 224-230.
[2]	ZHANG Xuejun1，2, WANG Longqiang1, HUANG Wanlu1, HUANG Liya1，2, CHENG Xiefeng1，2. EEG signals feature extraction based on EMD and CSP combined WOSF [J]. Computer Engineering and Applications, 2018, 54(24): 149-155.
[3]	GUO Rui, FAN Yamin. Algorithm based on extreme learning machine to restrain the end effect of BS-EMD and its application [J]. Computer Engineering and Applications, 2017, 53(7): 256-262.
[4]	LI Zhengxin, LIU Jianli, ZHOU Jian, GAO Weidong. Improvements of fabric defect segmentation based on bidimensional empirical mode decomposition [J]. Computer Engineering and Applications, 2016, 52(24): 217-222.
[5]	LI Zhezhu1，2, GAO Peixin3, TONG Kun4, ZHAO Dazhe1，2, LIU Jiren1，2. Research of fault diagnosis method of hydraulic pipeline cracks based on HHT [J]. Computer Engineering and Applications, 2016, 52(20): 221-226.
[6]	ZHAO Taotao, YANG Hongwu. Formant extraction algorithm of speech signal by combining EMD and WMCEP [J]. Computer Engineering and Applications, 2015, 51(9): 207-212.
[7]	GUAN Weiguo1, YAO Qingzhi1, LU Baochun2. HHT harmonic detection and time-frequency analysis method in microgrid [J]. Computer Engineering and Applications, 2015, 51(20): 198-202.
[8]	LIU Xiaowen, JIANG Lei, XU Hua. Ultra-wideband signal detection on Hilbert-Huang transform [J]. Computer Engineering and Applications, 2015, 51(12): 223-229.
[9]	BAO Guangqing, CHANG Yong, YANG Guojin. De-noising of rolling bearing fault vibration signal based on empirical mode decomposition threshold [J]. Computer Engineering and Applications, 2015, 51(10): 205-210.
[10]	JIAO Bei, ZENG Yicheng, MAO Yanhu. Pitch detection method based on Hilbert-Huang transform for speech signal [J]. Computer Engineering and Applications, 2015, 51(1): 204-207.
[11]	DU Xiaoqing, YU Fengqin. Speaker recognition algorithm based on HHT cepstrum coefficient [J]. Computer Engineering and Applications, 2014, 50(3): 198-202.
[12]	LIU Shen, ZHANG Xiaoji, NIU Yilong, WANG Pingping. Feature extraction and classification experiment of underwater acoustic signals based on energy spectrum of IMF’s [J]. Computer Engineering and Applications, 2014, 50(3): 203-206.
[13]	GONG Ying, LONG Wei, LI Meng, ZHANG Xiao. Blood cell recognition on combination of HHT and neural network [J]. Computer Engineering and Applications, 2014, 50(10): 266-270.
[14]	ZHANG Lina1，2, GUO Min1. Identification of wheat impact acoustic signal based on HHT [J]. Computer Engineering and Applications, 2013, 49(17): 213-216.
[15]	WANG Xuemin1, HUANG Fanglin2. Method to eliminate short-time strong disturbance from signal by using HHT [J]. Computer Engineering and Applications, 2012, 48(5): 131-134.

Study of objective visual EEG mode classification based on Hilbert-Huang transform

基于Hilbert-Huang变换的目标注视脑电模式分类研究

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics