Classification of ADHD/normal participants using frequency features of ERP's Independent Components

Abstract

This study investigates the Event Related Potentials (ERP) obtained from Independent Components of EEG (ERPIC) while participants performed a sustained attention task. EEG signals were recorded from 50 adult participants including ADHD and normal subjects while performing Continuous Performance Test (CPT). Signals were recorded from 21 Ag/AgCl electrodes according to the international 10–20 standard. Independent Component Analysis (ICA) was used as the processing method. For ERP extraction, average of each group of signals which were time-locked to the onset of stimuli was calculated. Several frequency features were extracted from different ERPICs. High accuracy (92%) was achieved in classification of clinical and non-clinical participants using combination of two features in a K-Nearest Neighbors (KNN) classifier. Nine pairs of features resulted in such accuracy, while most of the best features are related to the power in γ band which is consistent with the previous studies. Regarding the ERP groups, most of the best features are related to wrong answered targets and to time block ERPICs. The results revealed a promising relation between clinical situation of the participants and some parameters of brain independent components which can be used for further evaluations of the sustained attention level.