New interdependence feature of EEG signals as a biomarker of timing deficits evaluated in Attention-Deficit/Hyperactivity Disorder detection

Abstract

Similarity quantification is an important field of study in electroencephalogram (EEG)-based brain activity detection, in which the goal is to compute interdependence between certain cortical areas from inter-hemispheric or intra-hemispheric channel pairs. This study aims to propose a new interdependence EEG feature, namely Dynamic frequency warpping(DFW) based on dynamic analysis of frequency fluctuations as a hybrid feature extraction step. A new EEG classifier based on sparse coding has been developed for Attention Deficit Hyperactivity Disorder (ADHD) detection. It has been tested using EEG recordings of 14 ADHD children and 19 healthy controls during resting state and a time-reproduction task. The capability of the proposed method with an accuracy rate of 99.17% has been shown. Use of the DFW extracted from frontal channel pairs or beta frequency band not only improves the performance but also reduces the computational complexity due to the need to a subgroup of channels or a subband.