Abstract: M. Aceves-Fernandez (2021)

Dealing with electroencephalogram signals (EEG) is often not easy. The lack of predicability and complexity of such non-stationary, noisy and high-dimensional signals is challenging. Cross recurrence plots (CRP) have been used extensively to deal with the detection of subtle changes in signals, even when the noise is embedded in the signal. In this contribution, a total of 121 children performed visual attention experiments and a proposed methodology using CRP and a Welch Power Spectral Distribution have been used to classify then between those who have ADHD and the control group. Additional tools were presented to determine to which extent this methodology is able to classify accurately and avoid misclassifications, thus demonstrating that this methodology is feasible to classify EEG signals from subjects with ADHD. The experimental results indicate that the proposed methodology shows higher accuracy in comparison with methods proposed by other authors, providing that the correct recurrence tools are selected. Also, this methodology does not require extensive training such as the methods proposed using classical machine learning algorithms. Furthermore, this proposed methodology shows that it is not required to manually discriminate events among the EEG electrodes since CRP can detect even the smallest changes in the signal even when it has embedded noise. Lastly, the results were compared with baseline machine learning methods to prove experimentally that this methodology is consistent and the results repeatable. Given the right CRP metrics, an accuracy of up to 97.25% was achieved, indicating that this methodology outperformed many of the state-of-the-art techniques.

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Methodology proposal of ADHD classification of children based on cross recurrence plots