Received: 26 August 2024 | Revised: 28 October 2024 | Accepted: 4 November 2024 | Published online: 19 March 2025 RESEARCH ARTICLE AI Perspectives within Computational Neuroscience: EEG Integrations and the Human Brain Zarif Bin Akhtar 1, * and Victor Stany Rozario 2 1 Department of Computing, Institute of Electrical and Electronics Engineers (IEEE), USA 2 Department of Computer Science, Faculty of Science & Technology, American International University-Bangladesh (AIUB), Bangladesh Abstract: Current advancements within the realm of computational neuroscience, combined with the transformative capabilities of artificial intelligence (AI), have opened new paths for understanding the human brain’s interconnected complexity. This research exploration integrates electroencephalography (EEG), computational neuroscience, along with AI toward the investigation of complex cognitive mechanisms and neural activations associated with the various types of mental states. As a non-invasive tool, EEG mainly captures the internal electrical activity that reveals the interconnected cognitive processes in real time. By leveraging AI techniques—such as deep learning (DL), machine learning (ML), transfer learning, and convolutional neural networks (CNN)—this investigation deciphers EEG data to identify various specific neural patterns accompanying various types of cognitive states, memory formation, and especially toward emotional responses. To further refine these results and findings, this study organizes applications chronologically, presenting a developmental perspective on the AI-driven EEG advancements and their significance in detecting nuanced brain activity. This research not only addresses how experimental methods impact cognitive state reliability but also examines the amygdala’s role in EEG during emotional stimuli, thus expanding our multimodal level for understanding of emotional and memory-related neural signatures. By merging EEG data with AI-calibrated models, this investigation proposes new perspectives on the neural basis of attention, perception, and cognitive function, potentially informing early diagnosis of neurological disorders and enhancing brain-computer interfaces. Through this multidisciplinary lens, the exploration advances clinical applications and cognitive interventions, highlighting the interplay between EEG, computational neuroscience, and AI as an essential frontier in terms of both science and neurotechnology. Keywords: artificial intelligence, biomedical engineering, computational neuroscience, cognitive computing, deep learning, electroencephalography, machine learning 1. Introduction The homo sapiens epicenter of cognition retrospect the human mind, a repository of extraordinary complexity and with a wide range of leveling depth, has captivated scholars, scientists, and curious thinkers for many centuries. This complex organ, which is responsible for generating thoughts, emotions, memories, and most importantly human behaviors, shapes human experience and the uniqueness toward individual identity. However, these levels of complexities in terms of cognition and the underlying interconnected neural mechanisms still remain an enigma, sparking continuous inquiry across the diverse disciplines and domains alike. Today, as rapid technological advancements rush, the potential toward the decipher of these anonymities has reached unprecedented levels of apex expandability. At the forefront of this investigative exploration is the conjunction of electroencephalography (EEG), computational neuroscience, and artificial intelligence (AI)—a very powerful troika promising new and innovative insights into the neural basis of cognitive computing and its associated emotional processes [1–3]. EEG, a non-invasive technique for recording the brain’s electrical activity, provides a dynamic view of neural oscillations linked to various cognitive states and functions. By capturing real- time neural signals, EEG enables the examination of cognitive processes with a temporal resolution unattainable by other neuroimaging techniques. In the recent years, advancements in EEG technology have allowed for higher precision and spatial resolution, paving the way for more nuanced explorations of brain function [4–6]. This research leverages high-resolution EEG data to investigate key cognitive domains, including attention, memory formation, and emotion processing, exploring neural signatures associated with each state. AI has transfigured neuroscience by empowering the analysis of many large, complex datasets. Machine learning (ML) algorithms, including transfer learning and especially deep learning (DL) *Corresponding author: Zarif Bin Akhtar, Department of Computing, Institute of Electrical and Electronics Engineers (IEEE), USA. Email: zarifbinakhta r@ieee.org Artificial Intelligence and Applications 2025, Vol. 00(00) 1–16 DOI: 10.47852/bonviewAIA52024174 © The Author(s) 2025. Published by BON VIEW PUBLISHING PTE. LTD. This is an open access article under the CC BY License (https://creativecommons.org/ licenses/by/4.0/). 01