BRAIN COMPUTER INTERFACE SYSTEMS POWERED BY ARTIFICIAL INTELLEGENCE: EMERGING APPLICATIONS IN COGNITVE NEROSCIENCE

Abstract

Brain–Computer Interface (BCI) systems have become a revolutionary technology which allows direct communication between the human brain and external devices, thus presenting new opportunities in the field of healthcare and rehabilitation, as well as cognitive neuroscience (Wolpaw et al., 2002). Significant progress has been made in recent years with the development of Artificial Intelligence (AI) technologies, such as Machine Learning, Deep Learning, Transformer Models, Reinforcement Learning, and Explainable Artificial Intelligence (XAI), which has improved the performance and reliability of BCI systems, enabling better neural signal processing, feature extraction, and decision-making (Roy et al., 2019; Craik et al., 2019). The applications of BCI technology are no longer limited to assistive communication, but have been expanded by the integration of AI to encompass attention monitoring, cognitive workload assessment, memory enhancement, emotion recognition, neuro feedback training, neurological disorder diagnosis, and human–AI collaborative systems (Lotte et al., 2018; Shih et al., 2023). This paper follows a systematic review methodology to review the latest advancements in BCI systems powered by AI and their new applications in the field of cognitive neuroscience. The literature related to the subject was gathered from the year 2015 to 2026 from the reputable scientific databases.This review provides an analysis of BCI architectures, AI techniques, application domains, performance evaluation metrics, research challenges, and future directions. The results suggest that with the use of AI-driven BCI system, high classification accuracy and better cognitive evaluation and neuro rehabilitation results can be obtained (Lebedev & Nicolelis, 2017). Data scarcity and signal variability, privacy and security concerns, ethical considerations, model interpretability, and cross-subject generalization, however, remain challenges that hinder the widespread adoption of large-scale use (Saha & Baumert, 2020). The study also shows the potential for Generative AI, Explainable AI, Digital Brain Twins, Federated Learning, Quantum AI, and Metaverse integration as future research directions. Overall, AIdriven BCI systems hold great promise for revolutionizing cognitive neuroscience, intelligent healthcare and next-generation human–computer interaction technologies.