Classification of sustained attention level based on morphological features of EEG's independent components

The aim of this study is to investigate the relations between morphological features of ERP's independent components and visual sustained attention. Continuous Performance Test (CPT) is used for defining the level of sustained attention. Independent Component Analysis (ICA) is applied on 19-channel recorded EEG and the best component is determined based on time, frequency and spatial specifications of the components. The ERPs are extracted for each group of stimuli and eighteen morphological features (including P3) were extracted. Nineteen subjects were divided into three groups according to their attention level. LDA classifier is then used for discrimination of classes. The results are compared with two other common methods. Classification based on the proposed method yields in accuracy of 81% with the advantage of preserving almost all the data. Outcomes represent a significant correlation between CPT result and some parameters of brain signal's components which can be used in evaluating the level of attention.