The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the ‘clean’ EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.
In this paper, an improved empirical mode decomposition (EMD) algorithm for phonocardiogram (PCG) signal de-noising is proposed. Based on PCG signal processing theory, the S1/S2 components can be extracted by combining the improved EMD-Wavelet algorithm and Shannon energy envelope algorithm. Firstly, by applying EMD-Wavelet algorithm for pre-processing, the PCG signal was well filtered. Then, the filtered PCG signal was saved and applied in the following processing steps. Secondly, time domain features, frequency domain features and energy envelope of the each intrinsic mode function's (IMF) were computed. Based on the time frequency domain features of PCG's IMF components which were extracted from the EMD algorithm and energy envelope of the PCG, the S1/S2 components were pinpointed accurately. Meanwhile, a detecting fixed method, which was based on the time domain processing, was proposed to amend the detection results. Finally, to test the performance of the algorithm proposed in this paper, a series of experiments was contrived. The experiments with thirty samples were tested for validating the effectiveness of the new method. Results of test experiments revealed that the accuracy for recognizing S1/S2 components was as high as 99.75%. Comparing the results of the method proposed in this paper with those of traditional algorithm, the detection accuracy was increased by 5.56%. The detection results showed that the algorithm described in this paper was effective and accurate. The work described in this paper will be utilized in the further studying on identity recognition.
Electrocardiogram (ECG) signals are susceptible to be disturbed by 50 Hz power line interference (PLI) in the process of acquisition and conversion. This paper, therefore, proposes a novel PLI removal algorithm based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD). Firstly, according to the morphological differences in ECG waveform characteristics, the noisy ECG signal was decomposed into the mutated component, the smooth component and the residual component by MCA. Secondly, intrinsic mode functions (IMF) of PLI was filtered. The noise suppression rate (NSR) and the signal distortion ratio (SDR) were used to evaluate the effect of de-noising algorithm. Finally, the ECG signals were re-constructed. Based on the experimental comparison, it was concluded that the proposed algorithm had better filtering functions than the improved Levkov algorithm, because it could not only effectively filter the PLI, but also have smaller SDR value.
Early detection and timely intervention are very essential for autism. This paper used the wavelet transform and empirical mode decomposition (EMD) to extract the features of electroencephalogram (EEG), to compare the feature differences of EEG between the autistic children and healthy children. The experimental subjects included 25 healthy children (aged 5–10 years old) and 25 children with autism (20 boys and 5 girls aged 5–10 years old) respectively. The alpha, beta, theta and delta rhythm wave spectra of the C3, C4, F3, F4, F7, F8, FP1, FP2, O1, O2, P3, P4, T3, T4, T5 and T6 channels were extracted and decomposed by EMD decomposition to obtain the intrinsic modal functions. Finally the support vector machine (SVM) classifier was used to implement assessment of autism and normal classification. The results showed that the accuracy could reach 87% and which was nearly 20% higher than that of the model combining the wavelet transform and sample entropy in the paper. Moreover, the accuracy of delta (1–4 Hz) rhythm wave was the highest among the four kinds of rhythms. And the classification accuracy of the forehead F7 channel, left FP1 channel and T6 channel in the temporal region were all up to 90%, which expressed the characteristics of EEG signals in autistic children better.
This paper presents a feature extraction method based on multivariate empirical mode decomposition (MEMD) combining with the power spectrum feature, and the method aims at the non-stationary electroencephalogram (EEG) or magnetoencephalogram (MEG) signal in brain-computer interface (BCI) system. Firstly, we utilized MEMD algorithm to decompose multichannel brain signals into a series of multiple intrinsic mode function (IMF), which was proximate stationary and with multi-scale. Then we extracted and reduced the power characteristic from each IMF to a lower dimensions using principal component analysis (PCA). Finally, we classified the motor imagery tasks by linear discriminant analysis classifier. The experimental verification showed that the correct recognition rates of the two-class and four-class tasks of the BCI competitionⅢand competitionⅣreached 92.0% and 46.2%, respectively, which were superior to the winner of the BCI competition. The experimental proved that the proposed method was reasonably effective and stable and it would provide a new way for feature extraction.
Brain computer interface is a control system between brain and outside devices by transforming electroencephalogram (EEG) signal. The brain computer interface system does not depend on the normal output pathways, such as peripheral nerve and muscle tissue, so it can provide a new way of the communication control for paralysis or nerve muscle damaged disabled persons. Steady state visual evoked potential (SSVEP) is one of non-invasive EEG signals, and it has been widely used in research in recent years. SSVEP is a kind of rhythmic brain activity simulated by continuous visual stimuli. SSVEP frequency is composed of a fixed visual stimulation frequency and its harmonic frequencies. The two-dimensional ensemble empirical mode decomposition (2D-EEMD) is an improved algorithm of the classical empirical mode decomposition (EMD) algorithm which extended the decomposition to two-dimensional direction. 2D-EEMD has been widely used in ocean hurricane, nuclear magnetic resonance imaging (MRI), Lena image and other related image processing fields. The present study shown in this paper initiatively applies 2D-EEMD to SSVEP. The decomposition, the 2-D picture of intrinsic mode function (IMF), can show the SSVEP frequency clearly. The SSVEP IMFs which had filtered noise and artifacts were mapped into the head picture to reflect the time changing trend of brain responding visual stimuli, and to reflect responding intension based on different brain regions. The results showed that the occipital region had the strongest response. Finally, this study used short-time Fourier transform (STFT) to detect SSVEP frequency of the 2D-EEMD reconstructed signal, and the accuracy rate increased by 16%.
Fetal heart sound is nonlinear and non-stationary, which contains a lot of noise when it is colleced, so the denoising method is important. We proposed a new denoising method in our study. Firstly, we chose the preprocessing of low-pass filter with a cutoff frequency of 200 Hz and the re-sampling. Secondly, we decomposed the signal based on empirical mode decomposition method (EMD) of Hilbert-Huang transform, then denoised some selected target components with wavelet soft threshold adaptive noise cancellation algorithm. Finally we got the clean fetal heart sound by combining the target components. In the EMD, we used a mask signal to eliminate the mode mixing problem, used mirroring extension method to eliminate the end effect, and referenced the stopping rule from the research of Rilling. This method eliminated the baseline drift and noise at once. To compare with wavelet transform(WT), mathematical morphology (MM) and the Fourier transform (FT), the SNR was improved obviously, and the RMSE was the minimum, which could satisfy the need of the practical application.
Artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) seriously affect the reliability of shockable rhythm detection algorithms. In this paper, we proposed an adaptive CPR artifacts elimination algorithm without needing any reference channels. The clean electrocardiogram (ECG) signals can be extracted from the corrupted ECG signals by incorporating empirical mode decomposition (EMD) and independent component analysis (ICA). For evaluating the performance of the proposed algorithm, a back propagation neural network was constructed to implement the shockable rhythm detection. A total of 1 484 corrupted ECG samples collected from pigs were included in the analysis. The results of the experiments indicated that this method would greatly reduce the effects of the CPR artifacts and thereby increase the accuracy of the shockable rhythm detection algorithm.
The precise recognition of feature points of impedance cardiogram (ICG) is the precondition of calculating hemodynamic parameters based on thoracic bioimpedance. To improve the accuracy of detecting feature points of ICG signals, a new method was proposed to de-noise ICG signal based on the adaptive ensemble empirical mode decomposition and wavelet threshold firstly, and then on the basis of adaptive ensemble empirical mode decomposition, we combined difference and adaptive segmentation to detect the feature points, A, B, C and X, in ICG signal. We selected randomly 30 ICG signals in different forms from diverse cardiac patients to examine the accuracy of the proposed approach and the accuracy rate of the proposed algorithm is 99.72%. The improved accuracy rate of feature detection can help to get more accurate cardiac hemodynamic parameters on the basis of thoracic bioimpedance.
Ensemble empirical mode decomposition (EEMD) is an effective method for non-stationary signal analysis, such as electrocardiogram (ECG) signals. However, the precision and correctness of EEMD are affected by the two parameters, ratio of the added noise and ensemble number. The values of two parameters are set relying on experience and lacking of adaptability for uncertain signals. In order to solve these problems, we proposed a method based on white noise decomposed by EEMD in the present study shown in this paper. Empirical mode decomposition (EMD) was applied to decompose the signal to different intrinsic mode functions (IMFs) in the de-noising process. The white noise IMFs were selected to constitute high frequency part based on the character that the product of the energy density of white noise and its average period tended to be a constant. Then the two parameters of EEMD were adaptively obtained according to the criterion which was used to avoid modal aliasing. Experimental results showed that the method was an effective one for ECG signal de-noising.
