BRAIN-COMPUTER INTERFACE SYSTEMS USING ARTIFICIAL INTELLIGENCE: A REVIEW OF EEG-BASED APPROACHES

Abstract: Brain–Computer Interface (BCI) systems enable direct communication between the human brain and external devices by interpreting neural signals, primarily captured through Electroencephalography (EEG). With the increasing demand for assistive technologies, healthcare monitoring, and intelligent human–machine interaction, EEG-based BCI systems have gained significant attention in recent years. However, the inherent complexity, noise, and variability of EEG signals pose major challenges in achieving accurate and reliable signal classification.

Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML), particularly deep learning techniques, have significantly improved the performance of EEG-based BCI systems. Models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), hybrid CNN–LSTM architectures, and attention-based models have demonstrated strong capability in extracting spatial and temporal features from EEG signals.

This paper presents a comprehensive review of recent research developments (2020–2026) in AI-driven EEG-based BCI systems. It analyzes various models, techniques, and challenges, and highlights future research directions for improving system accuracy and real-time applicability.