Exploring the Evolution of Feature Extraction Methods in Brain–Computer Interfaces (BCIs): A Systematic Review of Research Progress and Future Trends

Abstract

Brain–computer interfaces (BCIs) have emerged as transformative tools, enabling direct communication between the brain and external devices, particularly for individuals with neuromuscular disabilities. This paper provides a comprehensive analysis of feature extraction (FE) methods across all major signal processing domains and various types of BCIs, addressing a significant gap in existing reviews and surveys that often focus exclusively on EEG‐based systems. Also, a detailed comparative analysis of FE techniques, highlighting their formulas, advantages, limitations, and practical applications, is provided. The study not only reviews state‐of‐the‐art methods but also evaluates recent research, identifying trends and gaps in the field. Key insights reveal a growing foundation for invasive BCI research, which, while currently limited, shows promise for future advancements. Based on this analysis, we identify and discuss open challenges such as inter‐subject variability, real‐time processing demands, integration of multiple modalities, and user training and adaptation. Additionally, we examine pressing concerns related to security, privacy, and the transferability of models. By addressing these challenges, this paper aims to guide the development of robust, efficient, and inclusive BCI systems, paving the way for cutting‐edge innovations and real‐world applications.This article is categorized under: Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction