TY - JOUR AU - Mohammadi, Mahdi AU - Al-Azab, Fadwa AU - Raahemi, Bijan AU - Richards, Gregory AU - Jaworska, Natalia AU - Smith, Dylan AU - de la Salle, Sara AU - Blier, Pierre AU - Knott, Verner PY - 2015 DA - 2015/12/23 TI - Data mining EEG signals in depression for their diagnostic value JO - BMC Medical Informatics and Decision Making SP - 108 VL - 15 IS - 1 AB - Quantitative electroencephalogram (EEG) is one neuroimaging technique that has been shown to differentiate patients with major depressive disorder (MDD) and non-depressed healthy volunteers (HV) at the group-level, but its diagnostic potential for detecting differences at the individual level has yet to be realized. Quantitative EEGs produce complex data sets derived from digitally analyzed electrical activity at different frequency bands, at multiple electrode locations, and under different vigilance (eyes open vs. closed) states, resulting in potential feature patterns which may be diagnostically useful, but detectable only with advanced mathematical models. SN - 1472-6947 UR - https://doi.org/10.1186/s12911-015-0227-6 DO - 10.1186/s12911-015-0227-6 ID - Mohammadi2015 ER -