Detecting lapses of attention using electroencephalography (EEG) signals

Detecting attention lapses during educational activities, particularly in the context of reading amid auditory distractions, is essential yet often overlooked in current research. In this thesis, we leveraged EEG signals to detect attentional lapses. The participants completed a reading task with and without auditory oddball distractions to establish two distinct attention levels which were validated through NASA TLX scores and reading comprehension outcomes. We compared three feature extraction techniques powerband, Common Spatial Patterns (CSP), and filterbank CSP across five classifiers. Filterbank CSP achieved the highest accuracy (over 90\%), with the beta band outperforming theta and alpha in classification tasks. Notably, classifier choice was less influential with CSP methods. Our reduced-channel classification results suggest the feasibility of using limited-channel EEG devices for future studies.