Agentic AI for EEG-Based Brain–Computer Interfaces: A Review of Methods, Systems, and Applications

Abstract

EEG-based Brain-Computer Interfaces (BCIs) have created a powerful communication channel for translating human neural activity into meaningful, structured commands to operate external devices or computers. This paper reviews EEG-based Brain-Computer Interfaces, focusing on advanced AI and agentic AI-based systems design and applications across 33 peer-reviewed papers published between 2015 and 2025. The papers demonstrate and review different methodologies spanning from traditional machine learning to advanced and then cutting-edge methods like Agentic AI approaches- including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Random Forests (RF), XGBoost, Artificial Neural Networks (ANN), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, Graph Neural Networks (GNN), Hybrid models, Reinforcement Learning. Study shows that among all methodologies, XGBoost achieved the highest 99% accuracy, and the second-most accurate CNN-based models achieved 83.8%–87.1% accuracy in the fields of attention monitoring, ADHD, driving safety, auditory attention/hearing, education, video tagging, and multi-tasking. These methodologies are deployed in wearable technologies (mostly EEG-based headsets) with single or multiple channels, with or without neurofeedback. However, this review identifies the transition from classification-based BCIs to fully autonomous BCIs agents powered by advanced agentic AI as the most critical future direction and reference for researchers and engineers in this field.