The ADHD effect on the high-dimensional phase space trajectories of EEG signals

Attention-deficit/hyperactivity disorder (ADHD) as a behavioral challenge, which affects the people's learning and experiences, is one of the disorders, which leads to reducing the complexity of brain processes and human behaviors. Nevertheless, recent studies often focused on the effects of this disorder in the frequency content of single and multichannel EEG segments and only a few studies that employed the approximate entropy for estimating this reduction. In this study, we provide a different view of this reduction by focusing on the texture of patterns appeared on the auto-recurrence plots obtained from the phase space trajectories reconstructed from the EEG signals recorded under the open-eyes and closed-eyes resting conditions. The outcomes of this analysis generally indicated a significant difference in the texture of recurrence plots, which its reason was the increase of recurrence, parallel and similar behaviors in the trajectories. Evaluating the features extracted from these recurrence plots in the studied children without and with ADHD using the sequential forward selection (SFS) algorithm also provided a remarkable accuracy (90.95% for the testing sets), which is a confirmation on changing the texture of recurrence plots relevant to the EEG signals of ADHD children. Nevertheless, evaluating these results and the results of previous researches with each other represented that the volume of statistical population is an important factor for reducing the rate of separability in the classifiers developed by an EEG segment. Therefore, these findings generally proved that although the ADHD averagely leads to the complexity reduction of EEG processes, the classifiers developed by just an EEG segment cannot be applicable in clinical conditions.