SUPERIOR CLASSIFICATION OF EEG STATES USING RECURRENCE QUANTIFICATION ANALYSIS

This study investigates the classification of brain states using electroencephalography (EEG) data, comparing recurrence quantification analysis (RQA) with traditional spectral analysis. The goal is to distinguish between eyes-open and eyes-closed states using EEG data. Experimental results demonstrate that RQA provides superior classification accuracy, particularly at the O1 electrode, where accuracy improved from 86% to 95%. The study also identifies optimal phase space reconstruction parameters and the most informative recurrence features for classification. RQA captures nonlinear dynamics of brain activity more effectively than frequency-based spectral methods. The findings support the use of RQA for improving classification in portable EEG systems. This enables more accurate analysis in real-time applications such as cognitive training and brain-computer interfaces.