TY - JOUR EP - 86 ID - scholars6176 KW - Brain; Data acquisition; Diagnosis; Efficiency; Frequency bands; Information science; Support vector machines KW - Average reference; Classification efficiency; Electro-encephalogram (EEG); Hemi-spheric asymmetries; Infinity reference; Link-ear reference; Major depressive disorder; Major depressive disorder (MDD) KW - Electroencephalography TI - A study to investigate different EEG reference choices in diagnosing major depressive disorder N2 - Choice of an electroencephalogram (EEG) reference is a critical issue during measurement of brain activity. An appropriate reference may improve efficiency during diagnosis of psychiatric conditions, e.g., major depressive disorder (MDD). In literature, various EEG references have been proposed, however, none of them is considered as gold-standard 1. Therefore, this study aims to evaluate 3 EEG references including infinity reference (IR), average reference (AR) and link-ear (LE) reference based on EEG data acquired from 2 groups: the MDD patients and healthy subjects as controls. The experimental EEG data acquisition involved 2 physiological conditions: eyes closed (EC) and eyes open (EO). Originally, the data were recorded with LE reference and re-referenced to AR and IR. EEG features such as the inter-hemispheric coherences, inter-hemispheric asymmetries, and different frequency bands powers were computed. These EEG features were used as input data to train and test the logistic regression (LR) classifier and the linear kernel support vector machine (SVM). Finally, the results were presented as classification accuracies, sensitivities, and specificities while discriminating the MDD patients from a potential population of healthy controls. According to the results, AR has provided the maximum classification efficiencies for coherence and power based features. The case of asymmetry, IR and LE performed better than AR. The study concluded that the reference selection should include factors such as underlying EEG data, computed features and type of assessment performed. © Springer International Publishing Switzerland 2015. SN - 03029743 AV - none SP - 77 PB - Springer Verlag N1 - cited By 0; Conference of 22nd International Conference on Neural Information Processing, ICONIP 2015 ; Conference Date: 9 November 2015 Through 12 November 2015; Conference Code:157039 A1 - Mumtaz, W. A1 - Malik, A.S. A1 - Ali, S.S.A. A1 - Yasin, M.A.M. UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951874614&doi=10.1007%2f978-3-319-26561-2_10&partnerID=40&md5=a81266dc86a54a0adebcf02f9b448366 Y1 - 2015/// VL - 9492 JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ER -