Advancements in Brain-Computer Interfaces: A Comprehensive Review of EEG-Based Mental Task Classification

Abstract

Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing human-computer interaction, enabling direct control of external devices using brain signals. Among the various signal acquisition methods, electroencephalography (EEG) has gained significant attention due to its non-invasive nature, portability, and high temporal resolution. This research article provides a comprehensive review of the advancements in EEG-based mental task classification, a critical component of BCI systems. It explores the fundamental principles, signal processing techniques, classification algorithms, and emerging trends in this field. The review covers the entire pipeline, from EEG signal acquisition and preprocessing to feature extraction, dimensionality reduction, and machine learning-based classification methods. Additionally, it discusses the challenges and limitations associated with EEG-based mental task classification, as well as future research directions to enhance the performance and applicability of BCI systems.