Abstract

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.

Highlights

  • Brain-Computer Interface (BCI) is a system being developed to connect the brain and a computer by using individual brainThe associate editor coordinating the review of this manuscript and approving it for publication was Samu Taulu.signals [1]

  • Improving the accuracy of the Motor Imagery (MI) EEG signal classification while reducing the BCI system complexity introduced by the number of channels utilized, we evaluate, for the first time to our knowledge, the effectiveness of multivariate empirical wavelet transform (MEWT) [40] for both channel selection and feature extraction for the multi-channel, non-stationary and nonlinear MI EEG signals

  • The performances of MEWT based experiments are measured by average sensitivity (Sen), specificity (Spe), classification accuracy (Acc), and area under the receiver operating characteristics (AUC) [62], and they are defined as follows, TP

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Summary

Introduction

Brain-Computer Interface (BCI) is a system being developed to connect the brain and a computer by using individual brainThe associate editor coordinating the review of this manuscript and approving it for publication was Samu Taulu.signals [1]. Sadiq et al.: MI EEG Signals Decoding by MEWT-Based Framework for Robust BCIs is the most commonly used practical opt for BCI systems for its robust nature [2], and various methods, e.g., positron emission tomography (PET), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), electrocorticography (ECoG) and electroencephalography (EEG) [2], [3], have been developed to monitor the MI signals Among all those techniques, EEG based MI BCI systems are the utmost used owing to their non-invasiveness, and capability of providing excellent temporal information of MI signals at a low cost [2], [4]. A big challenge for any realtime BCI system, is to correctly decode different MI EEG signals automatically [5]

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