基于噪声辅助快速多维经验模式分解的运动想象脑电信号分类方法

doi:10.11959/j.issn.2096-6652.202026

Abstract

Abstract:

The brain-computer interface is an emerging technology,which can analyze the collected motor imagery signals to control the external auxiliary equipment.A new method based on the noise-assisted fast multivariate empirical mode decomposition (NA-FMEMD) algorithm was proposed for electroencephalogram signal feature extraction and classification.The method outperformed state-of-the-art methods based on noise-assisted multivariate empirical mode decomposition in not only computational efficiency but also classification accuracy.Firstly,all multivariate intrinsic mode functions and trend signals were obtained by the NA-FMEMD.Secondly,the multivariate signals with specific frequency bands were selected by computing their average frequencies.Thirdly,the common spatial pattern was applied to extract features.Finally,the feature vectors were classified using a support vector machine.Simulation data and BCI Competition IV data are used to verify the effectiveness and advantage of the new method,and the method is compared with noise-assisted multivariate empirical mode decomposition.

Key words: electroencephalogram signals, motor imagery, noise-assisted fast multivariate empirical mode decomposition, common spatial pattern

CLC Number:

TP391

Qian ZHENG, Dan QIAO, Xun LANG, et al. Classification of motor imagery signals using noise-assisted fast multivariate empirical mode decomposition[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(3): 240-250.

Figures/Tables 13

References 29

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信号	β波/Hz	α波/Hz	θ波/Hz
x₁(t)	24.9/22.8/26.7/22.1	11.1/12.7/10.9/10.7	7.3/6.4/6.2/4.2
x ₂(t)	28.1/28.9/20.0/25.6	12.1/12.0/12.0/12.3	7.4/5.9/4.0/5.2
x ₃(t)	18.8/19.6/27.0/25.9	9.1/8.1/11.1/10.4	5.3/6.5/5.1/4.7
x ₄(t)	19.1/15.4/13.1/23.4	11.7/9.3/8.8/8.1	4.5/4.9/6.9/4.8

信号	d₁/Hz	d₂/Hz	d₃/Hz	d₄/Hz	d₅/Hz
x₁(t)	37.3/37.0/33.7/34.9	24.7/22.8/26.6/21.9	11.2/12.9/11.3/11.0	7.3/6.5/6.5/5.1	5.7/5.8/4.5/4.2
x ₂(t)	34.6/32.2/37.3/33.9	27.9/28.5/20.1/25.5	12.2/12.1/12.3/12.6	7.4/6.1/6.3/5.3	5.7/5.9/4.2/5.1
x ₃(t)	38.2/39.2/33.8/32.5	19.0/19.7/26.9/25.7	11.1/16.2/11.2/10.4	9.1/5.6/5.4/4.9	5.2/6.0/4.9/4.4
x ₄(t)	37.1/37.0/38.5/33.5	19.1/16.8/25.5/23.5	11.9/14.4/10.7/8.5	10.0/9.2/7.0/5.6	4.6/4.9/4.5/4.5

信号	d₁/Hz	d₂/Hz	d₃/Hz
x₁(t)	24.5/22.8/26.7/21.9	11.1/11.5/11.0/10.9	7.3/6.3/6.1/4.2
x ₂(t)	28.0/29.1/20.2/25.6	12.2/12.1/12.0/12.6	7.3/5.9/4.3/5.3
x ₃(t)	18.7/19.8/27.1/25.8	9.8/9.6/11.3/10.3	5.3/6.4/5.0/4.8
x ₄(t)	19.4/16.1/18.4/23.0	11.9/12.7/9.2/8.2	4.7/5.5/6.7/4.8

信号	d₁/Hz	d₂/Hz	d₃/Hz	d₄/Hz	d₅/Hz
x₁(t)	23.93	11.26	6.20	3.54	1.72
x ₂(t)	22.54	10.81	6.47	3.41	1.78
x ₃(t)	23.07	10.96	6.35	3.45	1.98
x ₄(t)	21.81	10.63	6.47	3.27	1.81

受试者	特征维度	基于NA-FMEMD的脑电信号分类方法	基于NA-MEMD的脑电信号分类方法
	m=1	70.0%	68.3%
	m=2	83.3%	78.3%
a	m=3	83.3%	85.0%
	m=4	85.0%	86.7%
	m=5	90.0%	81.7%
	m=1	71.7%	78.3%
	m=2	76.7%	75.0%
b	m=3	80.0%	73.3%
	m=4	78.3%	70.0%
	m=5	75.0%	68.3%
	m=1	60.0%	58.3%
	m=2	76.7%	80.0%
f	m=3	80.0%	78.3%
	m=4	80.0%	75.0%
	m=5	76.7%	73.3%
	m=1	68.3%	70.0%
	m=2	86.7%	75.0%
g	m=3	90.0%	86.7%
	m=4	90.0%	85.0%
	m=5	91.7%	88.3%

Classification of motor imagery signals using noise-assisted fast multivariate empirical mode decomposition

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受试者	分类方法	基于NA-FMEMD的脑电信号分类方法	基于NA-MEMD的脑电信号分类方法
	SVM	90.0%	86.7%
a	CART	80.0%	85.0%
	KNN	83.3%	83.3%
	FLD	93.3%	80.0%
	SVM	80.0%	78.3%
b	CART	66.7%	65.0%
	KNN	68.3%	76.7%
	FLD	86.7%	83.3%

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