EEG Based Motor Imagery Classification Using Instantaneous Phase Difference Sequence

Kumar, Satyam; Reddy, Tharun Kumar; Behera, Laxmidhar

doi:10.1109/smc.2018.00094

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…In [24], an instantaneous phase difference method was implemented to obtain phase-based features by extraction of phase synchrony information among different electrodes; however, the success rate could be further improved with more sophisticated extraction techniques. In literature different spectral signal representation techniques have been used to explore discriminative features for MI EEG signal classification and results suggest that power spectral density (PSD) provides reasonable success rates in comparison with energy distribution, atomic decompositions and wavelet based techniques [25].…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

et al. 2019

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The robustness and computational load are the key challenges in motor imagery (MI) based on electroencephalography (EEG) signals to decode for the development of practical brain-computer interface (BCI) systems. In this study, we propose a robust and simple automated multivariate empirical wavelet transform (MEWT) algorithm for the decoding of different MI tasks. The main contributions of this study are four-fold. First, the multiscale principal component analysis method is utilized in the preprocessing module to obtain robustness against noise. Second, a novel automated channel selection strategy is proposed and then is further verified with comprehensive comparisons among three different strategies for decoding channel combination selection. Third, a sub-band alignment method by utilizing MEWT is adopted to obtain joint instantaneous amplitude and frequency components for the first time in MI applications. Four, a robust correlation-based feature selection strategy is applied to largely reduce the system complexity and computational load. Extensive experiments for subject-specific and subject independent cases are conducted with the three-benchmark datasets from BCI competition III to evaluate the performances of the proposed method by employing typical machine-learning classifiers. For subject-specific case, experimental results show that an average sensitivity, specificity and classification accuracy of 98% was achieved by employing multilayer perceptron neural networks, logistic model tree and least-square support vector machine (LS-SVM) classifiers, respectively for three datasets, resulting in an improvement of upto 23.50% in classification accuracy as compared with other existing method. While an average sensitivity, specificity and classification accuracy of 93%, 92.1% and 91.4% was achieved for subject independent case by employing LS-SVM classifier for all datasets with an increase of up to 18.14% relative to other existing methods. Results also show that our proposed algorithm provides a classification accuracy of 100% for subjects with small training size in subject-specific case, and for subject independent case by employing a single source subject. Such satisfactory results demonstrate the great potential of the proposed MEWT algorithm for practical MI EEG signals classification.

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Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

et al. 2019

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“…One more restriction is that CSP is more oriented to power-based features [41], so that its validity of the combination with PLV is questionable, as the achieved results definitely indicate. The combination of CSP with CCF and GFC allows for enhancing the performed accuracy, but in DBI MI and DBII ME with a relatively moderate number of subjects.…”

Section: Discussionmentioning

confidence: 99%

“…For comparative purposes, we also perform feature extraction using the baseline CSPbased spatial filtering that is widely used for filtering measures of synchronization [40,41]. To this end, we perform the CSP feature extraction, adjusting the sliding time window length at each evaluated value of τ and fixing the variance of the surrogate space to the first three eigenvectors of the spatial filtering matrix, as suggested in [42].…”

Section: Experimental Set-upmentioning

confidence: 99%

Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

García-Murillo

Álvarez-Meza

Castellanos-Domínguez

2021

Sensors

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Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.

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“…Authors in [13] coupled the notion of variance of instantaneous phasors with sPLV. Furthermore, they formulated a framework to estimate a linear transform that maximizes the variance of instantaneous phasors across one class while simultaneously minimizing it across the other class.…”

Section: Differential-phase Synchrony Representationsmentioning

confidence: 99%

“…Caramia et al [12] proposed a modified single trial PLV but it did not account for lead lag behavior between EEG signals. Kumar et al [13] proposed to extract features based on Instantaneous Phase Difference (IPD) for trial wise analysis. Further, [14] explored Mean Phase Coherence (MPC) with large and local scale synchrony for fatigue detection.…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram Based Reaction Time Prediction With Differential Phase Synchrony Representations Using Co-Operative Multi-Task Deep Neural Networks

Reddy

Arora

Kumar

et al. 2019

IEEE Trans. Emerg. Top. Comput. Intell.

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Driver drowsiness is receiving a lot of deliberation as it is a major cause of traffic accidents. This paper proposes a method which utilizes the fuzzy common spatial pattern optimized differential phase synchrony representations to inspect electroencephalogram (EEG) synchronization changes from the alert state to the drowsy state. EEG-based reaction time prediction and drowsiness detection are formulated as primary and ancillary problems in the context of multi-task learning. Statistical analysis results suggest that our method can be used to distinguish between alert and drowsy state of mind. The proposed Multi-Task DeepNet (MTDNN) performs superior to the baseline regression schemes, like support vector regression (SVR), least absolute shrinkage and selection operator, ridge regression, K-nearest neighbors, and adaptive neuro fuzzy inference scheme (ANFIS), in terms of root mean squared error (RMSE), mean absolute percentage error (MAPE), and correlation coefficient (CC) metrics. In particular, the best performing multi-task network MTDNN 5 recorded a 15.49% smaller RMSE, a 27.15% smaller MAPE, and a 10.13% larger CC value than SVR.

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EEG Based Motor Imagery Classification Using Instantaneous Phase Difference Sequence

Cited by 15 publications

References 18 publications

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces

Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

Electroencephalogram Based Reaction Time Prediction With Differential Phase Synchrony Representations Using Co-Operative Multi-Task Deep Neural Networks

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