Using non-linear features of EEG for ADHD/normal participants’ classification

AbstractThis study investigates the non-linear features of electroencephalogram signals regarding ADHD and normal adult participants while performing Continuous Performance Test. Three non-linear features were extracted from the EEG signals. ADHD and age-matched normal groups were investigated separately which revealed that there is a significant relation between clinical presentation of the participants and some non-linear features. The accuracy of 88% and 96% were achieved in classification of clinical and non-clinical participants using one and two features respectively. The best classification result was obtained with a combination of two features in Wavelet-Entropy group