Understanding Cognitive Cerebral Synergies: Leveraging R Analytics to Uncover AI and BIAI Insights

Abstract

This study is an interdisciplinary research project that integrates Human Cognitive Neuroscience, Artificial Intelligence (AI), Brain-Inspired Artificial Intelligence (BIAI), and Data Analytics. Cognitive neuroscience examines the cognitive-cerebral synergies that involve complex interactions and coordination among brain regions involved in mental processes. AI and BIAI help analyse brain activity patterns and further develop an accurate predictive model. The use of R analytics acts as a catalyst in overall data analysis, data visualisation, and result modelling. In this study, R code produces graphical outputs for Brain Network Visualisation, AI-Inspired Neural Network Visualisation, Synergy Analysis Heatmap, Cognitive Load Visualisation, and AI Performance Comparison. This study focuses exclusively on understanding brain function and on developing AI models inspired by it. The study has applications in Brain-Computer Interfaces (BCIs), Cognitive Enhancement, and personalised medicine for the treatment of specific diseases. The quantitative research methodology of this study uses neuroimaging data from functional magnetic resonance imaging (fMRI), electroencephalography (EEG), or magnetoencephalography (MEG), which provide deeper insight into brain activity in healthy respondents. Such data can be processed using R packages such as caret, dplyr, and ggplot2, with emulated R code. This research methodology reports valuable insights into cognitive cerebral synergies and applies them in developing brain-inspired AI systems. The study aims to bridge the gap between cognitive neuroscience and AI. The future scope of this study may include integrating fMRI, EEG, and BCI to develop practical solutions for neurological disorders and disabilities in individuals.