Cross-Subject Motor Imagery Tasks EEG Signal Classification Employing Multiplex Weighted Visibility Graph and Deep Feature Extraction

This letter presents a novel technique for classification of motor imagery (MI) electroencephalogram (EEG)
signals employing a multiplex weighted visibility graph (MWVG) algorithm. A weighted visibility graph (WVG) is an
effective tool to map a univariate time series into a graphical representation while preserving its temporal characteristics.
In this contribution, the concept of WVG of univariate time series is extended to analyze multivariate EEG time series
known as a MWVG algorithm. From the graphical representation of the transformed EEG time series, a new method
for construction of complex functional brain connectivity network using clustering co-efficient was proposed based on
mutual correlation between different electrodes. An auto encoder based deep feature extraction technique was employed
to extract meaningful features from the images of brain connectivity matrix and classification of different MI tasks was
performed using different benchmark classifiers. In this contribution, a cross-subject classification is performed to address
the problem of lack of generalized features from EEG signals across different subjects. It was observed that an average
classification accuracy of 99.92% and 99.96% is obtained using the Random Forest classifier. Experimental investigations
on two publicly available databases revealed that the proposed model can be implemented to develop a robust and
effective brain computer interface system