부산대학교 도서관

The paper presents chaos-theory-based human attention level detection from the electroencephalogram (EEG) signals. In the medical field, “human attention level” can be referred to as the “attentional state,”, which helps to understand an individual's attention capacity in various crucial moments. In this study, we have deployed secondary analysis on existing methods by implementing chaos theory on the PhysioNet dataset. We investigate different vital parameters and values to predict human attention level from the EEG dataset. We calculated time delay, embedding dimension, and correlation dimension from the participants' EEG data to determine the parameters' values: specifically, for detecting of human attention level from EEG signals. By calculating the 95% confidence interval (CI), the time delay has an average of 2.50 seconds, and the embedding dimension and correlation dimension have an average value of 4.41 and 2.23, respectively. We also observed a similar embedded signal in the reconstructed phase space (RPS) of participant's EEG signals. The statistical and chaos-based plot can potentially investigate human attention parameters and develop a robust EEG signal prediction system. Overall, this proposed framework serves as a resource on the latest nonlinearity detection techniques to detect human attention levels utilizing EEG signal analysis. Clinical Relevance - The effectiveness of the chaos-based non-linear analysis method for detecting human attention levels in EEG signals depends on its potential impact on diagnosis and treatment, integration into clinical practice, benefits, and risks.