The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface

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.

An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets

“…Recently proposed MI tasks classification method using CWT and CNN by Chaudhary et al [ 30 ] achieved an ACC of 99.35%. Subasi and Qaisar [ 38 ] developed a hybrid model combing hybridization of MSPCA and WPD to extract the SB. Six statistical measures extracted from the SB have been classified using ensemble classifier techniques.…”

“…An automated form of variational mode decomposition combined with extreme learning machine classifier has been also used to detect MI tasks [ 37 ]. In [ 38 ], the authors explored hybridization of MSPCA combined with WPD to extract hidden information from EEG using subbands. The utility of flexible analytic wavelet transform (FAWT) has been used to extract and classify the evaluated time-frequency features from the subbands using linear discriminant analysis classifier [ 39 ].…”

Hyperspectral Image Classification: Potentials, Challenges, and Future Directions

“… They have a wide range of data types that they can accommodate, for example, 2D imagery data and complex 3D data such as medical imagery and remote sensing. In addition, they can use HSI data's spectral and spatial domains in both standalone and linked ways [ 106 – 108 ]. They provide architects a lot of versatility in terms of layer types, blocks, units, and depth.…”

Arithmetic Optimization with RetinaNet Model for Motor Imagery Classification on Brain Computer Interface

“…All the sensors in the measurement samples have n sampling data, that is integrated into a data matrix of size mxn . The procedure has been shown as follows [ 2 ]: …”

“…The human brain produces electrical signal that is identified by using EEG. Therefore, it is highly reliable and applicable method for receiving the control command for BCI [ 2 ]. Studies involving EEG signals when imagining limb or finger movement, widely called motor imagery (MI), to function artificial intelligence (AI) technique has been witnessed in this study [ 3 ].…”