Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features

Author:

Sweeti 1ORCID,Joshi Deepak1,Panigrahi B. K.2ORCID,Anand Sneh1,Santhosh Jayasree3

Affiliation:

1. Centre for Biomedical Engineering, IIT Delhi, New Delhi, India

2. Department of Electrical Engineering, IIT Delhi, New Delhi, India

3. Department of Computer Engineering & Computer Science, Manipal International University, Putra Nilai, Malaysia

Abstract

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.

Funder

The Ministry of Human Resource Development, Government of India

Publisher

Hindawi Limited

Subject

Health Informatics,Biomedical Engineering,Surgery,Biotechnology

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