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

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 nonstationary multicomponent 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. We have also proposed time–frequency representation based on MSSDM to analyze discriminative cognitive patterns of different visual object classes from multichannel 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 outperformed all existing state-of-the-art methods used for visual object detection and achieved the highest accuracy of 86.42% using tenfold cross-validation on EEG-MEG multichannel signals.