Estimation of Brain Connectivity during Motor Imagery Tasks using Noise-Assisted Multivariate Empirical Mode Decomposition

Lee, Ki-Baek; Kim, Ko Keun; Song, JaeSeung; Ryu, Jiwoo; Kim, Youngjoo; Park, Cheolsoo

doi:10.5370/jeet.2016.11.6.1812

Cited by 12 publications

(6 citation statements)

References 35 publications

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“…Although previous literatures have reported the successful applications of EMD/EEMD in the single-channel EMGs [ 17 ], these approaches cannot solve the critical problem about the fusion and analysis of multichannel EMG signals [ 30 , 34 , 37 ]. Therefore, EEMD, MEMD and NA-MEMD have been investigated in this study for the decomposition performance of four knee muscle groups associated with standing, sitting, and walking.…”

Section: Discussionmentioning

confidence: 99%

Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals

Zhang

et al. 2017

BioMed Eng OnLine

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BackgroundEnsemble Empirical Mode Decomposition (EEMD) has been popularised for single-channel Electromyography (EMG) signal processing as it can effectively extract the temporal information of the EMG time series. However, few papers examine the temporal and spatial characteristics across multiple muscle groups in relation to multichannel EMG signals.ExperimentThe experimental data was obtained from the Center for Machine Learning and Intelligent Systems, University of California Irvine (UCI). The data was donated by the Nueva Granada Military University and the Technopark node Manizales in Colombia. The databases of 11 male subjects from the healthy group were taken into the study. The subjects undergo three exercise programs, leg extension from a sitting position (sitting), flexion of the leg up (standing), and gait (walking), while four electrodes were placed on biceps femoris (BF), vastus medialis (VM), rectus femoris (RF), and semitendinosus (ST).MethodsBased on the experimental data, a comparative study is provided by assessing the Empirical Mode Decomposition (EMD)-based approaches, EEMD, Multivariate EMD (MEMD), and Noise-Assisted MEMD (NA-MEMD). The outcomes from these approaches are then quantitatively estimated on the basis of three criterions, the number of Intrinsic Mode Functions (IMFs), mode-alignment and mode-mixing.ResultsBoth MEMD and NA-MEMD methods (except EEMD) can guarantee equal numbers of IMFs. For mode-alignment and mode-mixing, NA-MEMD is optimal compared with MEMD and EEMD, and MEMD is merely better than EEMD.ConclusionsThis study proposes the NA-MEMD approach for multichannel EMG signal processing. This finding implies that NA-MEMD is effective for simultaneously analysing IMFs based frequency bands. It has a vital clinical implication in exploring the neuromuscular patterns that enable the multiple muscle groups to coordinate while performing the functional activities of daily living.

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Section: Discussionmentioning

confidence: 99%

Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals

Zhang

et al. 2017

BioMed Eng OnLine

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“…Visual recognition is also not suitable for individuals who have lost their sight naturally. Thus, recent studies commonly suggest that MI is a more appropriate strategy [23][24][25][26]. Rig Das et al proposed EEG-based biometric identification, using a convolutional neural network (CNN) known as one of deep learning method omitting feature extraction procedures [24].…”

Section: Introductionmentioning

confidence: 99%

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

Bak

Jeong

2023

IEEE Access

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Human brain activities-electroencephalogram (EEG) signals-are likely to provide a secure biometric approach for user identification because they are more sensitive, secretive, and difficult to replicate. Many studies have recently focused on identifying and quantifying important frequency patterns in motor imagery (MI), recorded through EEG. However, there is still a lack of an optimal methodology for recognizing users with EEG-based MI. Therefore, we aimed to propose an EEG-MI methodology that utilizes optimized feature extraction methods and classifiers to improve user-aware accuracy. To accomplish this goal, we extracted four features related to MI and compared the accuracies for recognizing users using a support vector machine (SVM) and Gaussian Naïve Bayes (GNB). We then used the half-total error rate (HTER) to determine whether the results were reliable due to an imbalance problem caused by the differences in the data sizes. Thus, we used a common spatial pattern (CSP) to achieve the highest user identification accuracies of 98.97% and 97.47% using SVM and GNB, respectively. All user recognition accuracies are guaranteed by the HTERs, which are below 0.5. However, CSP has the disadvantage of decreasing accuracy on a small dataset scale. Therefore, we proposed and tested a statistical methodology for estimating a minimum dataset scale to ensure CSP performance. We confirm that the used dataset adequately guarantees CSP performance. This study makes a great contribution to the field of information security by presenting an EEG-MI methodology that improves the identification accuracy in human biometrics based on EEG-MI signals.

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“…The brain activity recorded via EEG can be classified depending on the frequency of the signal. In particular, the alpha activity (8)(9)(10)(11)(12) and the beta activity (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) are mostly related to MI [3]. In addition, the increase or decrease in the activity at a certain frequency band locked to the onset of the MI refer to the event-related synchronization and desynchronization (ERS/ERD), respectively [4].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, through the analysis of neural connectivity in the brain, the general function and communication between different regions of the brain are described. For example, Liang et al [15] and Lee et al [16] proved the functional connectivity in the process of motion imagination planning. Gong et al proposed a brain network modeling method based on timefrequency cross mutual information of four classes of MI.…”

Section: Introductionmentioning

confidence: 99%

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

Wang

Tang

et al. 2020

IEEE Access

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Motor imagery (MI) based brain-computer interface (BCI) is a research hotspot and has attracted lots of attention. Within this research topic, multiple MI classification is a challenge due to the difficulties caused by time-varying spatial features across different individuals. To deal with this challenge, we tried to fuse brain functional connectivity (BFC) and one-versus-the-rest filter-bank common spatial pattern (OVR-FBCSP) to improve the robustness of classification. The BFC features were extracted by phase locking value (PLV), representing the brain interregional interactions relevant to the MI, whilst the OVR-FBCSP is used to extract the spatial-frequency features related to the MI. These diverse features were then fed into a multi-kernel relevance vector machine (MK-RVM). The dataset with three motor imagery tasks (left hand MI, right hand MI, and feet MI) was used to assess the proposed method. Experimental results not only showed that the cascade structure of diverse feature fusion and MK-RVM achieved satisfactory classification performance (average accuracy: 83.81%, average kappa: 0.76), but also demonstrated that BFC plays a supplementary role in the MI classification. Moreover, the proposed method has a potential to be integrated into multiple MI online detection owing to the advantage of strong timeefficiency of RVM. INDEX TERMS Multiple motor imagery, filter-bank common spatial pattern (FBCSP), phase locking value (PLV), brain functional connectivity (BFC), multi-kernel relevance vector machine (MK-RVM).

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Estimation of Brain Connectivity during Motor Imagery Tasks using Noise-Assisted Multivariate Empirical Mode Decomposition

Cited by 12 publications

References 35 publications

Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals

Noise-assisted multivariate empirical mode decomposition for multichannel EMG signals

User Biometric Identification Methodology via EEG-Based Motor Imagery Signals

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

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