Comparative Study on Denoising Methods of EEG Signals Based on Subcomponent Decomposition
FU Rong-rong1,BAO Tian-tian1,TIAN Yong-sheng1,WANG Lin2
1. School of Electrical Engineering, Yanshan University, Qinghuangdao, Hebei 066004, China
2. School of Mechanical Engineering, Shenyang Institute of Technology, Shenyang, Liaoning 110136, China
Abstract：EEG signals contain abundant time and spatial information. In order to obtain good real-time and anti-interference physiological signals, a large number of weak EEG signal extraction techniques have been applied. In view of the fact that the actual physiological EEG signals are susceptible to irrelevant noise and other issues, the maximum signal fraction analysis and independent component analysis methods are compared to remove the artifacts contained in the EEG signals and the real-time performance of the processing. The effect of the solution is evaluated from the three aspects of scatter plot analysis, correlation index comparison and calculation speed. The results show that both methods have the effect of separating noise signals and can guarantee the real-time performance. However, maximum signal fraction analysis has better separation effect than independent component analysis, higher correlation index, higher similarity, similarity value floating smaller advantages of a more stable, and has a wider range of application prospects.
付荣荣,鲍甜恬,田永胜,王琳. 基于子成分分解的脑电信号去噪方法比较研究[J]. 计量学报, 2019, 40(4): 708-713.
FU Rong-rong,BAO Tian-tian,TIAN Yong-sheng,WANG Lin. Comparative Study on Denoising Methods of EEG Signals Based on Subcomponent Decomposition. Acta Metrologica Sinica, 2019, 40(4): 708-713.
［1］于向洋,罗志增. 基于小波系数非线性连续函数衰减的脑电信号去噪［J］. 计量学报, 2017,38(6):754-757.
Yu X Y,Luo Z Z. EEG Signal Denoising Based on a Wavelet Nonlinear Continuous Function［J］. Acta Metrologica Sinica, 2017, 38(6): 754-757.
［2］胡春海,信思旭,刘斌,等. 基于小波变换和盲源分离的P300识别算法研究［J］. 计量学报, 2017,38(2):242-246.
Hu C H, Xin S X, Liu B,et al. Study on Recognition Algorithm of P300 Based on Wavelet Transform and Blind Source Separation［J］. Acta Metrologica Sinica, 2017, 38(2):242-246.
［3］Ahirwal M K, Kumar A, Singh G K. Improved range selection method for evolutionary algorithm based adaptive filtering of EEG/ERP signals［J］. Neurocomputing, 2014, 144(1): 282-294.
［4］Santos L D, Barroso J J, Macau E E N, et al. Application of an automatic adaptive filter for Heart Rate Variability analysis［J］. Medical Engineering and Physics, 2013, 35(12): 1778-1785.
［5］张威,王旭,葛琳琳,等.一种强噪声背景下的微弱信号检测的新方法［J］.计量学报, 2007, 28(1): 70-73.
Zhang W, Wang X, Ge L L,et al. An Algorithm for Weak Signal Detection in Strong Noise Environment［J］. Acta Metrologica Sinica, 2007, 28(1): 70-73.
［6］Houssem C, Keisuke K. Using scale space filtering to make thinning algorithms robust against noise in sketch images［J］. Pattern Recognition Letters, 2014, 42(1): 1-10.
［7］胡进,侯增广,陈翼雄,等.下肢康复机器人及其交互控制方法［J］.自动化学报, 2014, 40(11): 2377-2390.
Hu J, Hou Z G, Chen Y X,et al. Lower Limb Rehabilitation Robots and Interactive Control Methods［J］. Acta Automatica Sinica, 2014, 40(11): 2377-2390.
［8］付荣荣,侯培国,时培明,等.基于子空间分解的脑电信号眼电伪迹自动去除方法研究［J］.计量学报,2017, 38(6): 749-753.
Fu R R, Hou P G, Shi P M,et al. Automatic EOG Artifact Elimination of EEG based on subspace decomposition［J］. Acta Metrologica Sinica, 2017, 38(6): 749-753.
［9］李松,伏云发,杨秋红,等.基于左右手运动想象单通道脑电信号的预处理研究［J］.生物医学工程学杂志, 2016,(5): 862-866.
Li S, Fu Y F, Yang Q H,et al. Pretreatment Research Based on Left and Right Hand Motor Imagery for Single-channel Electroencephalogram［J］. Journal of Biomedical Engineering, 2016,(5): 862-866.
［10］Fu R, Wang H. Detection of driving fatigue by using noncontact EMG and ECG signals measurement system［J］. International Journal of Neural Systems, 2014, 24(3): 90-20.
［11］Wang H. Temporally Local Maximum Signal Fraction Analysis for Artifact Removal From Biomedical Signals［M］. IEEE Press, 2010.
［12］Lu X S, Li X M, Fu M S, et al. Robust Maximum Signal Fraction Analysis for Blind Source Separation［J］. IET Signal Processing, 2017, 11(8): 969-974.
［13］Zhao D, Jiang J, Wang C, et al. FPGA Implementation of FastICA Algorithm for On-line EEG Signal Separation［C］// CCF National Conference on Computer Engineering and Technology. Springer, Berlin, Heidelberg, 2014: 59-68.
［14］Zakeri M, Zakeri Z. Evaluation of Independent Component Analysis Algorithms for Electroencephalography Source Separation［C］// International Conference on Human-Computer Interaction. Springer Berlin Heidelberg, 2013.
2019, Vol. 40 
