Improved Sparse Representation based Robust Hybrid Feature Extraction Models with Transfer and Deep Learning for EEG Classification

Numerous studies in the field of cognitive research is dependent on Electroencephalography (EEG) as it apprehends the neural correspondences of various mental activities such as memory, speech, or attention related activities with millisecond precision. In this work, initially sparse rendition is imposed on EEG signals and then sparse optimization is performed to it. For sparse representation, initially the work utilized both K-Singular Value Decomposition (K-SVD) for dictionary learning along with Orthogonal Matching Pursuit (OMP) for sparse coding. Later an advanced OMP technique was developed which made a significant improvement, and then instead of K-SVD, K-means and Method of Optimal direction (MOD) techniques were utilized with it and as a result, totally six different combinations in sparse representation optimization were developed. Then, it is modelled into clusters using seven different hybrid models developed here for the purpose of feature extraction and selection. Out of the seven models, two models are already existing such as Finite Mixture Logistic Regression Model (FMLR) and Expectation-Maximization based Gaussian Mixture Model (EM-GMM) which are widely used. The remaining five hybrid models are proposed as variations of GMM such as Variational Bayesian Matrix Factorization (VBMF) based GMM (VBMF-GMM), VBMF and Probabilistic Principal Component Analysis (PCA) with GMM (VBMF-PCA-GMM), VBMF and Partial Least Squares (PLS) with GMM (VBMF-PLS-GMM), VBMF and Canonical Correlation Analysis (CCA) with GMM (VBMF-CCA-GMM) and Empirical VBMF with GMM (EVBMF-GMM). This model is validated on two unique EEG datasets, such as epilepsy dataset and schizophrenia dataset, and the results have been classified with the standard machine learning techniques, transfer learning techniques and the proposed deep learning techniques leading to a total of twelve different classifiers and a comprehensive analysis is made in this work with very promising results in terms of classification accuracy reporting more than 95% for most of the proposed cases.