Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index.

Muhammad Tariq Sadiq,Muhammad Zulkifal Aziz,Xiaojun Yu,Zhaohui Yuan

doi:10.3390/s20185283

Muhammad Tariq Sadiq, Muhammad Zulkifal Aziz + Show 2 more

Open Access

https://doi.org/10.3390/s20185283

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Abstract

The development of fast and robust brain–computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems.

Highlights

With the rampant growth in automated systems, computer-aided physical systems, and artificial intelligence, brain–computer interface (BCI) has gained significant attention from researchers as it can bind a human mind to the computer and operate complex physical applications
As motor imagery EEG signals are highly dependent upon subject physical and mental nature so for each subject, different channels are selected by the automated channel selection criteria
For support vector machine (SVM) classifier, three different kernels namely radial basis function (RBF), linear kernel and the polynomial kernel were utilized, for Discriminative analysis (DA) classifier, linear, pseudo quadratic and pseudo linear kernels were adopted and their performance was evaluated for both datasets individually

Summary

Introduction

With the rampant growth in automated systems, computer-aided physical systems, and artificial intelligence, brain–computer interface (BCI) has gained significant attention from researchers as it can bind a human mind to the computer and operate complex physical applications. The healthcare realm has been overwhelmed by the development of computer-aided brain devices, namely, prosthetic arms, brain-controlled wheelchairs, mind-controlled home automation, etc., for physically impaired people [1,2,3,4,5,6,7]. The fundamental source of BCI is the low-key signal generated on the surface of the human scalp as a result of neural activity and it acts as a watershed for the plethora of brain-controlled applications. The common practices involved to retrieve such signals are invasive and noninvasive methods

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