Investigation of frequency components embedded in EEG recordings underlying neuronal mechanism of cognitive control and attentional functions.

Attentional cognitive control regulates the perception to enhance human behaviour. The current study examines the atltentional mechanisms in terms of time and frequency of EEG signals. The cognitive load is higher for processing local attentional stimulus, thereby demanding higher response time (RT) with low response accuracy (RA). On the other hand, the global attentional mechanisms broadly promote the perception while demanding a low cognitive load with faster RT and high RA. Attentional mechanisms refer to perceptual systems that afford and allocate the adaptive behaviours for prioritizing the processing of relevant stimuli based on the local and global features. The early sensory component of C1, which was associated with the local attentional mechanism, showed higher amplitudes than the global attentional mechanisms in parieto-occipital regions. Further, the local attentional mechanisms were also sustained in N2 and P3 components increasing higher amplitude in the left and right hemispheric sides of temporal regions (T7 and T8). Theta band frequency had shown higher power spectrum density (PSD) values while processing local attentional mechanisms. However, the significance of other frequency bands was noticeably minute. Hence, integrating the attentional mechanisms in terms of ERP and frequency signatures, a hybrid custom weight allocation model (CWAM) was built to assess and predict the contribution of insignificant channels to significant ones. The CWAM model was formulated based on the computational linear regression derivatives. All the derivatives are computationally derived the significant score while channelizing the hierarchical performance of each channel with respect to the frequent and deviant occurrences of global-local stimulus. This model enables us to configure the neural dynamicity of cognitive allocation of resources within the different locations of the human brain while processing the attentional stimulus. CWAM is reported to be the first model to evaluate the performance of the non-significant channels for enhancing the response of significant channels. The findings of the CWAM model suggest that the brain's performance may be determined by the underlying contribution of the non-significant channels. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09888-x.