Boosting Cognitive Focus via Binary Search of Attention Types Using Brain-Computer Interfaces

Electroencephalograms (EEGs) are widely used for analyzing brain signals, with one of their applications being the detection of attention and concentration levels. This study explores the potential of Brain-Computer Interfaces (BCIs) to enhance cognitive focus in children and teenagers, aged 8–17 years. Data was acquired anonymously to ensure participant privacy. This study aims to evaluate the potential of Brain-Computer Interfaces (BCIs) to improve cognitive focus in children and teenagers by detecting their attention state(effective and ineffective), which shows if a person's concentration refers to learning, another productive task or focusing on distractions. Effective and ineffective attention states are thus classified through EEG signals in our system with a 94% accuracy obtained by preprocessing, feature extracting with CSP and classifying with KNN, followed by the attention enhancement system that aims to help the patient recover their concentration by running a continuous binary search algorithm on 5 dimensions, which are external factors: temperature, lightning, smell, noise and font size