Automated Classification of Cognitive Visual Objects Using Multivariate Swarm Sparse Decomposition from Multichannel EEG-MEG Signals

Abstract

<p>In visual object decoding, magnetoencephalogram (MEG) and electroencephalogram (EEG) activation patterns demonstrate the utmost discriminative cognitive analysis due to their multivariate oscillatory nature. However, high noise in the recorded EEG-MEG signals and subject-specific variability make it extremely difficult to classify subject’s cognitive responses to different visual stimuli. The proposed method is a multivariate extension of the swarm-sparse decomposition method (MSSDM) for multivariate pattern analysis of EEG-MEG-based visual activation signals. In comparison, it is an advanced technique for decomposing non-stationary multi-component signals into a finite number of channel-aligned oscillatory components that significantly enhance visual activation-related sub-bands. The MSSDM method adopts multivariate swarm filtering and sparse spectrum to automatically deliver optimal frequency bands in channel-specific sparse spectrums, resulting in improved filter banks. By combining the advantages of the multivariate SSDM and Riemann’s correlation-assisted fusion feature (RCFF), the MSSDM-RCFF algorithm is investigated to improve the visual object recognition ability of EEG-MEG signals. A proposed MSSDM is evaluated on multivariate synthetic signals and multivariate EEG-MEG signals using five classifiers. The proposed fusion feature and linear discriminant analysis classifier-based framework (MSSDM-FF-LDA) outperformed all existing state-of-the-art methods used for visual object detection and achieved the highest accuracy of 81.82% using 10-fold cross-validation on EEG-MEG multichannel signals. </p>