Multivariate Intrinsic Mode Functions Research Articles

Optimizing feature subset for schizophrenia detection using multichannel EEG signals and rough set theory.

Schizophrenia (SZ) is a mental disorder that causes lifelong disorders based on delusions, cognitive deficits, and hallucinations. By visual assessment, SZ diagnosis is time-consuming and complicated, because brain states are more effectively revealed by electroencephalogram (EEG) signals, which are effectively used in SZ diagnosis. The application of existing deep learning methods in SZ detection is effective in the classification of 2-dimensional images, and these methods require more computational resources. Therefore, dimensionality reduction is necessary for SZ diagnosis using EEG signals. To reduce the dimensionality of the data, an improved CAO (ICAO) dimensionality reduction method is proposed, which integrates horizontal and vertical crossover approaches with AOA. The optimal feature subset is achieved by satisfying the ICAO conditions, and a fitness function is evaluated based on rough sets for improved accuracy in feature selection. Therefore a Crossover-boosted Archimedes optimization algorithm (AOA) with rough sets for Schizophrenia detection (CAORS-SD) was proposed using multichannel EEG signals from both SZ and normal patients. The signals are decomposed using multivariate empirical mode decomposition into multivariate intrinsic mode functions (MIMFs). Entropy metrics such as spectral entropy, permutation entropy, approximate entropy, sample entropy, and SVD entropy are evaluated on the MIMF domain to detect SZ. The processing time of the kernel support vector machine classifier is minimized with fewer features, reducing the risk Fof overfitting. Accuracy, sensitivity, specificity, precision, and F1-score of the CAORS-SD model should be conducted to diagnose SZ. Therefore, the proposed CAORS-SD method achieves the higher performance of accuracy, sensitivity, specificity, precision, and F1-score values of 96.34, 98.95, 96.86, 98.52, and 96.74% respectively. Also, the CAORS-SD method minimizes the error rate and significantly reduces the execution time.

Classifying Routine Clinical Electroencephalograms with Multivariate Iterative Filtering and Convolutional Neural Networks.

Electroencephalography (EEG) is a non-invasive tool widely used in basic and clinical neuroscience to explore neural states in various populations, and classifying these EEG recordings is a fundamental challenge. While machine learning shows promising results in classifying long multivariate time series, optimal prediction models and feature extraction methods for EEG classification remain elusive. Our study addressed the problem of EEG classification under the framework of brain age prediction, applying a deep learning model on EEG time series. We hypothesized that decomposing EEG signals into pseudostationary waveforms would yield more accurate age predictions than using raw or canonically frequency-filtered EEG. Specifically, we employed multivariate intrinsic mode functions (MIMFs), an empirical mode decomposition (EMD) variant based on multivariate iterative filtering (MIF), with a convolutional neural network (CNN) model. Testing a large dataset of routine clinical EEG scans (n = 6540) from patients aged 1 to 103 years, we found that an ad-hoc CNN model without fine-tuning could reasonably predict age from EEGs. Crucially, MIMF decomposition significantly improved performance compared to canonical brain rhythms (from delta to lower gamma oscillations). Our approach achieved a mean absolute error (MAE) of 13.76 ± 0.33 and a correlation coefficient of 0.64 ± 0.01 in brain age prediction over the entire lifespan. Our findings indicate that CNN models applied to EEGs, preserving their original temporal structure, remains a promising framework for EEG classification, wherein the time-frequency decompositions such as the MIF can enhance CNN models' performance in this task.

Electroencephalogram-Based Motor Imagery Brain–Computer Interface Using Multivariate Iterative Filtering and Spatial Filtering

In motor imagery (MI) based brain-computer interface (BCI), common spatial pattern (CSP) is most popularly used for discriminant feature extraction. However, the performance of CSP depends on the operational frequency bands, which are selected manually or set to a broad frequency range in most of the previously developed applications. Due to subject to subject or even trial to trial variability of frequency band affected by MI task, these methods suffer from poor performance. We have proposed a novel approach, using combination of multivariate iterative filtering (MIF) and CSP (MIFCSP), to automatically select optimal frequency bands based on MIF which can be further used for discriminant feature extraction. MIF decomposes the signal into several multivariate intrinsic mode functions, from which features are extracted using CSP. We select the minimum number of most significant features for which highest classification accuracy is achieved. Subsequently, linear discriminant analysis (LDA) classifier is used to classify different MI tasks. Experimental results for BCI competition IV dataset 2a and BCI competition III-IIIa are presented. For left hand versus right hand MI classification, proposed MIFCSP method provides 83.18% and 84.44% average accuracy, respectively. Superior classification performance confirms that MIFCSP is a promising candidate for MI BCI application.

Adaptive noise suppression for low-S/N microseismic data based on ambient-noise-assisted multivariate empirical mode decomposition

Microseismic monitoring data may be seriously contaminated by complex and nonstationary interference noises produced by mechanical vibration, which significantly impact the data quality and subsequent data-processing procedure. One challenge in microseismic data processing is separating weak seismic signals from varying noisy data. To address this issue, we proposed an ambient-noise-assisted multivariate empirical mode decomposition (ANA-MEMD) method for adaptively suppressing noise in low signal-to-noise (S/N) microseismic data. In the proposed method, a new multi-channel record is produced by combining the noisy microseismic signal with preceding ambient noises. The multi-channel record is then decomposed using multivariate empirical mode decomposition (MEMD) into multivariate intrinsic mode functions (MIMFs). Then, the MIMFs corresponding to the main ambient noises can be identified by calculating and sorting energy percentage in descending order. Finally, the IMFs associated with strong interference noise, high-frequency and low-frequency noise are filtered out and suppressed by the energy percentage and frequency range. We investigate the feasibility and reliability of the proposed method using both synthetic data and field data. The results demonstrate that the proposed method can mitigate the mode mixing problem and clarify the main noise contributors by adding additional ambient-noise-assisted channels, hence separating the microseismic signal and ambient noise effectively and enhancing the S/Ns of microseismic signals.

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of “vaginal vs. cesarean section” delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

An Efficient and Robust Muscle Artifact Removal Method for Few-Channel EEG

The unavoidable muscle artifacts pose challenges on reliable interpretation of the electroencephalogram (EEG) recordings, especially for the wearable few-channel EEG, a new emerging scenario. However, the high computational load and low robustness of the existing methods limit its wider applications and performance in artifact removal. Consequently, we propose an efficient and robust muscle artifact removal approach by jointly employing the Fast Multivariate Empirical Mode Decomposition (FMEMD) and CCA for few-channel EEG. The proposed FMEMD-CCA firstly efficiently decomposes the input EEG recordings into several multivariate Intrinsic Mode Functions (IMF) by applying FMEMD. Secondly, all the multivariate IMFs are processed by CCA for computing the underlying sources. Finally, the sources with low autocorrelations are smartly determined as muscle artifacts and rejected, and therefore the other components are reconstructed for EMG-artifact-free IMFs and EEG. Simulated and real data experiments are carried out for verifying the performance of the proposed method. It takes 10 times less computing time in FMEMD-CCA compared with in Multivariate Empirical Mode Decomposition (MEMD)-CCA for 10-s EEG recordings, using the same computer and software. And the accuracy and the average correlation coefficient are highly consistent in both approaches. Furthermore, in contrast to MEMD-CCA, the proposed FMEMD-CCA works more robustly in low sampling rate based on the real data and benchmark.

A Multivariate Empirical Mode Decomposition–Based Data-Driven Approach for Extracting Task-Dependent Hemodynamic Responses in Olfactory-Induced fMRI

Olfactory dysfunction is related to several clinical neurodegenerative diseases, such as Alzheimer's disease, multiple sclerosis, degenerative ataxias, Parkinson's disease, and so on. Owing to the individual difference in the sensory adaption of human smell function, the olfactory responses usually exhibit large inter-individual difference and change over time after repeated stimulations. The traditional analysis tools, such as statistical parametric mapping (SPM) in functional magnetic resonance imaging technique (fMRI) analysis, utilize the paradigm-based linear correlation and statistical techniques to discriminate the activation areas from background activities. However, these traditional approaches extract olfactory-induced responses using the stereotypic template or paradigm generated model. The olfactory-induced hemodynamic responses affected by internal/external events, such as changes of smell fatigue and attention, are not considered and therefore could result in misleading results. In this paper, owing to the stochastic characteristic of olfactory-induced responses, we adopted multivariate empirical mode decomposition (MEMD) to extract olfactory-induced hemodynamic responses in MRI blood-oxygen-level dependent (BOLD) signals. The MEMD is an efficient data-driven approach to extract nonlinear and non-stationary signals, which is an improved method to expand traditional empirical mode decomposition (EMD) from one channel to multi-channel processing. We applied MEMD to decompose time series of BOLD signals from each slice into multivariate intrinsic mode functions (IMF). The MEMD enables common features of different scales in an image slice to be arranged in distinct IMFs. Each IMF is an analytic, self-constructed, well-defined, and data-driven function with time-varying frequencies. Each IMF was examined by checking its correlation with the paradigm-generated template. The task-related IMFs were chosen to reconstruct olfactory-induced hemodynamic responses. The group analysis of MEMD-processed data showed olfactory-induced activations in the anterior cingulate cortex and middle frontal gyrus.

The Use of Multivariate EMD and CCA for Denoising Muscle Artifacts From Few-Channel EEG Recordings

Electroencephalography (EEG) recordings are often contaminated by muscle artifacts. In the literature, a number of methods have been proposed to deal with this problem. Yet most denoising muscle artifact methods are designed for either single-channel EEG or hospital-based, high-density multichannel recordings, not the few-channel scenario seen in most ambulatory EEG instruments. In this paper, we propose utilizing interchannel dependence information seen in the few-channel situation by combining multivariate empirical mode decomposition and canonical correlation analysis (MEMD-CCA). The proposed method, called MEMD-CCA, first utilizes MEMD to jointly decompose the few-channel EEG recordings into multivariate intrinsic mode functions (IMFs). Then, CCA is applied to further decompose the reorganized multivariate IMFs into the underlying sources. Reconstructing the data using only artifact-free sources leads to artifact-attenuated EEG. We evaluated the performance of the proposed method through simulated, semisimulated, and real data. The results demonstrate that the proposed method is a promising tool for muscle artifact removal in the few-channel setting.

A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry

A brain-computer interface (BCI) facilitates a medium to translate the human motion intentions using electrical brain activity signals such as electroencephalogram (EEG) into control signals. EEG signals are non-stationary and subject specific. A major challenge in BCI research is to classify human motion intentions from non-stationary EEG signals. We propose a novel subject specific multivariate empirical mode decomposition (MEMD) based filtering method, namely, SS-MEMDBF to classify the motor imagery (MI) based EEG signals into multiple classes. The MEMD method simultaneously decomposes the multichannel EEG signals into a group of multivariate intrinsic mode functions (MIMFs). This decomposition enables us to extract the cross-channel information and also localize the specific frequency information. The MIMFs are considered as narrow-band, amplitude and frequency modulated (AFM) signals. The statistical measure, mean frequency has been used to automatically filter the MIMFs to obtain enhanced EEG signals which better represent motor imagery related brainwave modulations over μ and β rhythms. The sample covariance matrix has been computed and used as a feature set. The feature set has been classified into multiple MI tasks using Riemannian geometry. The proposed method has helped achieve mean Kappa value of 0.60 across nine subjects of the BCI competition IV dataset 2A which is superior to all the reported methods.

Noise-assisted MEMD based relevant IMFs identification and EEG classification

Noise-assisted multivariate empirical mode decomposition (NA-MEMD) is suitable to analyze multichannel electroencephalography (EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation. For a finite set of multivariate intrinsic mode functions (IMFs) decomposed by NA-MEMD, it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way, and conventional approaches address it by use of prior knowledge. In this work, a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance (JSD) measure. A criterion of effective factor based on JSD was applied to select significant IMF scales. At each decomposition scale, three kinds of JSDs associated with the effective factor were evaluated: between IMF components from data and themselves, between IMF components from noise and themselves, and between IMF components from data and noise. The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.

A Novel Approach for the Analysis of Multi-Channel EEG Signal using Advance Technique

Objectives: In this research paper, Electroencephalogram (EEG) is recorded by placing electrodes on the scalp for different mental task. The significant features are extracted for three different metal tasks as mental arithmetic, baseline and letter composing. Methods/Statistical analysis: In the EEG signals, there are many features which having some significant information and some having false. The significant features are extracted by using advance techniques as Multivariate Empirical Mode Decomposition (MEMD) and Hilbert-Huang Transform (HHT). The t-paired test is used for determining the discrimination power of extracted features. Findings: After applying MEMD techniques we have achieved twelve multivariate Intrinsic Mode Functions (IMFs) and one residue. Most sensitive IMFs are selected by calculating Power Spectral Density (PSD) of each IMFs functions by Welch method. The Instantaneous Amplitude (IA) and Instantaneous Phase (IP) from most sensitive IMF are investigated by using Hilbert Huang transform (HHT) and features such as min., max., Skewness and kurtosis are extracted from IA and IP. The feature values are tested for their class discrimination power (p< .05) using paired t test. The results of paired t test support their applicability to be used as feature vector for any classification application. Accuracy nearby 80% to 90% is procured for different mental task EEG signals by using these extracted features. Application/Improvements: The investigated results are applicable for Brain Computer Interface. If a handicapped person (his hands and legs are not working/living) wants to write some letters on the screen of a desktop, so by putting the electrode on scalp and by measuring the electrical signals of his/her brain through EEG, we can apply these significant features for converting the electrical signals in to letters with the help of computer. In this research, we have investigated only linear feature, so further research area is open for investigating of Non-linear features of EEG signals.

The circular Bedrosian identity and multidimensional periodic analytic signals

The analytic signal method via the Hilbert transform is a classical manner of defining without ambiguity the instantaneous amplitude and frequency of one-dimensional signals. In real applications such as image processing, signals are usually of finite duration and belong to higher dimensions. Extension of this method to the multidimensional space is needed. We shall justify that partial Hilbert transforms can be used to define multidimensional periodic analytic signals. The important associated circular Bedrosian identity for partial Hilbert transforms is then studied, where f and g are multidimensional periodic signals. Several characterizations and a necessity theorem are established. These characterizations provide us an efficient way of constructing a larger class of multivariate intrinsic mode functions with nonlinear phases, which are key ingredients for the celebrated empirical mode decomposition.

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.