Selective visual attention is the ability to selectively pay attention to the targets while inhibiting the distractors. This paper aims to study the targets and non-targets interplay in spatial attention task while subject attends to the target object present in one visual hemifield and ignores the distractor present in another visual hemifield. This paper performs the averaged evoked response potential (ERP) analysis and time-frequency analysis. ERP analysis agrees to the left hemisphere superiority over late potentials for the targets present in right visual hemifield. Time-frequency analysis performed suggests two parameters i.e. event-related spectral perturbation (ERSP) and inter-trial coherence (ITC). These parameters show the same properties for the target present in either of the visual hemifields but show the difference while comparing the activity corresponding to the targets and non-targets. In this way, this study helps to visualise the difference between targets present in the left and right visual hemifields and, also the targets and non-targets present in the left and right visual hemifields. These results could be utilised to monitor subjects' performance in brain-computer interface (BCI) and neurorehabilitation.
This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.