Analysis of Adaptability in Brain-Computer Interfaces

This project addresses the persistent challenge of variability in the bioelectrical brain signals (EEG) used in Brain-Computer Interfaces (BCI). This variability, caused by factors such as fatigue, concentration, or genetic differences between subjects, degrades system control accuracy. The main objective is to create a methodology that mitigates this instability, facilitating the creation of user-adaptable interfaces. The research is structured in three phases. Initially, a solid theoretical framework will be established through an exhaustive review of the state-of-the-art in BCI. Subsequently, an experimental method will be applied to test and compare various BCI algorithms and modalities, searching for patterns that define adaptable systems. Finally, deduction and induction methods will be used to identify the key factors determining the efficiency of specific algorithms, aiming to contribute to the development of intelligent and more robust BCIs.<br/><br/><b>Goal</b>: <br/>To develop a methodology that manages variability in Brain-Computer Interfaces (BCI) through the implementation of computationally efficient and low-cost algorithms.<br/><br/><b>Research lines</b>: <br/>Telematics applied to medicine