Quantifying mode mixing and leakage in multivariate empirical mode decomposition and application in motor imagery–based brain-computer interface system

2019 ◽  
Vol 57 (6) ◽  
pp. 1297-1311 ◽  
Author(s):  
Yang Zheng ◽  
Guanghua Xu
Keyword(s):  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Interface System ◽  
Mode Mixing

scholarly journals Adaptive-projection intrinsically transformed multivariate empirical mode decomposition in cooperative brain–computer interface applications

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150199 ◽  
Author(s):  
Apit Hemakom ◽  
Valentin Goverdovsky ◽  
David Looney ◽  
Danilo P. Mandic
Keyword(s):  
Empirical Mode Decomposition ◽  
Information Transfer ◽  
Multiscale Analysis ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Analysis Framework ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Information Transfer Rate ◽  
Multichannel Data

An extension to multivariate empirical mode decomposition (MEMD), termed adaptive-projection intrinsically transformed MEMD (APIT-MEMD), is proposed to cater for power imbalances and inter-channel correlations in real-world multichannel data. It is shown that the APIT-MEMD exhibits similar or better performance than MEMD for a large number of projection vectors, whereas it outperforms MEMD for the critical case of a small number of projection vectors within the sifting algorithm. We also employ the noise-assisted APIT-MEMD within our proposed intrinsic multiscale analysis framework and illustrate the advantages of such an approach in notoriously noise-dominated cooperative brain–computer interface (BCI) based on the steady-state visual evoked potentials and the P300 responses. Finally, we show that for a joint cognitive BCI task, the proposed intrinsic multiscale analysis framework improves system performance in terms of the information transfer rate.

EEG Classification of Imaginary Left and Right Leg Movements based on Multivariate Empirical Mode Decomposition and Non-Linear Support Vector Machine

2021 ◽  
Vol 39 (11) ◽  
Author(s):  
Sahar Zolfaghari ◽  
Mohammad Hamiruce Marhaban ◽  
Siti Anom Ahmad ◽  
Asnor Juraiza Ishak ◽  
Pegah Khosropanah ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
Motor Imagery ◽  
Classification Model ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Non Linear ◽  
Left And Right

Motor-imagery brain-computer interfaces, as rehabilitation tools for motor-disabled individuals, could inherently enrich neuroplasticity and subsequently restore mobility. However, this endeavour's significant challenge is classifying left and right leg motor imagery tasks from non-stationary EEG signals. A subject-independent feature extraction method is essential in a BCI system, and this work involves developing a subject-independent algorithm to classify left/right leg motion intention. The Multivariate Empirical Mode Decomposition was used to decompose EEG during left and right foot movements during imagery tasks. We validated our proposed algorithm using open-access motor imagery data to detect the user's mental intention from EEG. Five subjects of various performance categories with almost 150 trials for each left/right leg MI of hand/leg/tongue, HaLT Paradigm, utilizing C3, C4, and Cz channels were examined to generalize this study to all subjects. A set of statistical features were extracted from the intrinsic mode functions, and the most relevant features were selected for classification using Sequential Floating Feature Selection. Different classifiers were trained using extracted features, and their performances' were evaluated. The findings suggest that the non-linear support vector machine is the best classification model, resulting in the mean classification sensitivity, specificity, precision, negative predictive value, F-measure, 98.15%, 90.74%, 91.97%, 98.33%, 94.72%, 94.44%, respectively. The proposed subject-independent signal processing method significantly improved the offline calibration mode by eliminating the frequency selection step, making it the common-used method for different types of MI-based BCI participants. Offline evaluations suggest that it can lead to significant increases in classification accuracy in comparison to current approaches.

scholarly journals Tangent Space Features-Based Transfer Learning Classification Model for Two-Class Motor Imagery Brain–Computer Interface

2019 ◽  
Vol 29 (10) ◽  
pp. 1950025 ◽  
Author(s):  
Pramod Gaur ◽  
Karl McCreadie ◽  
Ram Bilas Pachori ◽  
Hui Wang ◽  
Girijesh Prasad
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Tangent Space ◽  
Brain Computer Interface ◽  
Training Session ◽  
Training Data ◽  
Classification Model ◽  
Computer Interface ◽  
Multivariate Empirical Mode Decomposition ◽  
Subject Specific

The performance of a brain–computer interface (BCI) will generally improve by increasing the volume of training data on which it is trained. However, a classifier’s generalization ability is often negatively affected when highly non-stationary data are collected across both sessions and subjects. The aim of this work is to reduce the long calibration time in BCI systems by proposing a transfer learning model which can be used for evaluating unseen single trials for a subject without the need for training session data. A method is proposed which combines a generalization of the previously proposed subject-specific “multivariate empirical-mode decomposition” preprocessing technique by taking a fixed band of 8–30[Formula: see text]Hz for all four motor imagery tasks and a novel classification model which exploits the structure of tangent space features drawn from the Riemannian geometry framework, that is shared among the training data of multiple sessions and subjects. Results demonstrate comparable performance improvement across multiple subjects without subject-specific calibration, when compared with other state-of-the-art techniques.

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