A Novel Event-Related Neural Activity Extraction Technique by Fusing Neural Oscillation and Neural Synchronization

Previous research has proposed a number of techniques for the extraction of single-trial event-related neural activity (ERNA). However, these single-trial extraction techniques did not simultaneously consider about neural oscillation and neural synchronization, thereby creating an opportunity to optimize the single-trial extraction technique. In this study, based on the neural basis of electroencephalography (EEG), a novel single-trial extraction technique, which fuses neural oscillation and neural synchronization, was proposed. The neural activity extracted by this technique was identified as a novel type of ERNA and defined as connectivity-related neural activity (CRNA). The CRNA from the single-trial EEG was extracted through the utilisation of independent component analysis (ICA), non-negative matrix factorization (NNMF), and the alternating direction method of multipliers (ADMM). Furthermore, the performance of CRNA was evaluated using two key measures : signal-to-noise ratio (SNR) and Pearson's correlation coefficient (RHO). To investigate the effect of electrode density on CRNA performance, we compared five standard electrode configurations (including 9, 16, 32, 64, and 128 channel configurations). It was observed that there was a notable enhancement in the SNR of CRNA with an increase in electrode density. Finally, the comparative analysis demonstrated that the SNR and RHO of CRNA exhibited superior performance compared to several existing single-trial extraction techniques, including the conventional wavelet-based approach, the translation-invariant (TI) wavelet-based approach, the NZT, and the Generalized Subspace Approach (GSA). The proposed method facilitated the optimization of the single-trial extraction technique.