Abstract
The Brain-Computer Interface (BCI) permits persons with impairments to interact with the real world without using the neuromuscular pathways. BCIs are based on artificial intelligence piloted systems. They collect brain activity patterns linked to the mental process and transform them into commands for actuators. The potential application of BCI systems is in the rehabilitation centres. In this context, a novel method is devised for automated identification of the Motor Imagery (MI) tasks. The contribution is an effective hybridization of the Multiscale Principal Component Analysis (MSPCA), Wavelet Packet Decomposition (WPD), statistical features extraction from subbands, and ensemble learning-based classifiers for categorization of the MI tasks. The intended electroencephalogram (EEG) signals are segmented and denoised. The denoising is achieved with a Daubechies algorithm-based wavelet transform (WT) incorporated in the MSPCA. The WT with the 5th level of decomposition is used. Onward, the Wavelet Packet Decomposition (WPD), with the 4th level of decomposition, is used for subbands formation. The statistical features are selected from each subband, namely, mean absolute value, average power, standard deviation, skewness, and kurtosis. Also, ratios of absolute mean values of adjacent subbands are computed and concatenated with other extracted features. Finally, the ensemble machine learning approach is used for the classification of MI tasks. The usefulness is evaluated by using the BCI competition III, MI dataset IVa. Results revealed that the suggested ensemble learning approach yields the highest classification accuracies of 98.69% and 94.83%, respectively, for the cases of subject-dependent and subject-independent problems.
Highlights
A Brain-Computer Interface (BCI) allows individuals to use electroencephalogram (EEG) signals to operate external equipment such as virtual worlds, robots, or spelling machines. e fundamental objective of the BCI is to use brain signals to create the required commands to control peripherals. e most important application is to bypass injured areas of the body or stimulate partly paralyzed organs
Zarei et al [9] used a combination of the Principal Component Analysis (PCA) and the cross-covariance (CCOV) method for features extraction from the EEG signals for the BCI application. e multilayer perceptron neural networks (MLP) and Least Square Support Vector Machine (LS-SVM) are used for classification. e performance of the system is tested by using the BCI competitions dataset IVa
While considering the case of each subject, the highest accuracy of 98.69% is achieved by the Random Subspace Method (RSM) with Random Forest (RF)
Summary
Received 20 August 2021; Revised 30 September 2021; Accepted 25 October 2021; Published 9 November 2021. E potential application of BCI systems is in the rehabilitation centres. In this context, a novel method is devised for automated identification of the Motor Imagery (MI) tasks. E contribution is an effective hybridization of the Multiscale Principal Component Analysis (MSPCA), Wavelet Packet Decomposition (WPD), statistical features extraction from subbands, and ensemble learning-based classifiers for categorization of the MI tasks. E denoising is achieved with a Daubechies algorithm-based wavelet transform (WT) incorporated in the MSPCA. E usefulness is evaluated by using the BCI competition III, MI dataset IVa. Results revealed that the suggested ensemble learning approach yields the highest classification accuracies of 98.69% and 94.83%, respectively, for the cases of subject-dependent and subject-independent problems
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