Decoding brain signals: A comprehensive review of EEG-Based BCI paradigms, signal processing and applications

Brain-computer interface (BCI) based on electroencephalography (EEG) is a fast-developing field with a wide range of applications such as assistive technology, neurorehabilitation, entertainment, cognitive enhancement, etc. Since EEG is a non-invasive technique that captures brain activity in real time, it is ideally suited for developing interfaces that enable direct brain-to-device communication. The different paradigms utilised in EEG-based BCIs, such as Motor Imagery (MI), Steady-State Visual Evoked Potentials (SSVEP), P300 Event-related Potentials (ERP), and Hybrid paradigms that integrate several strategies for enhanced performance, are the main emphasis of this systematic review. This paper also explores the signal processing techniques, feature extraction strategies, and classification algorithms necessary for handling low-amplitude and noisy EEG recordings. The applications of BCI in different fields, as well as the challenges and possible solutions of EEG-based BCIs, are also covered in this article. Overall, the state-of-the-art in EEG-based BCIs is thoroughly reviewed in this comprehensive review article, which also identifies important areas for further study and technological advancement.