title: Independent component approach to the analysis of EEG recordings at early stages of depressive disorders.
abstract: OBJECTIVE
  A modern approach for blind source separation of electrical activity represented by Independent Components Analysis (ICA) was used for QEEG analysis in depression.
  
  
  METHODS
  The spectral characteristics of the resting EEG in 111 adults in the early stages of depression and 526 non-depressed subjects were compared between groups of patients and healthy controls using a combination of ICA and sLORETA methods.
  
  
  RESULTS
  Comparison of the power of independent components in depressed patients and healthy controls have revealed significant differences between groups for three frequency bands: theta (4-7.5Hz), alpha (7.5-14Hz), and beta (14-20Hz) both in Eyes closed and Eyes open conditions. An increase in slow (theta and alpha) activity in depressed patients at parietal and occipital sites may reflect a decreased cortical activation in these brain regions, and a diffuse enhancement of beta power may correlate with anxiety symptoms playing an important role on the onset of depressive disorder.
  
  
  CONCLUSIONS
  ICA approach used in the present study allowed us to localize the EEG spectra differences between the two groups.
  
  
  CONCLUSIONS
  A relatively rare approach which uses the ICA spectra for comparison of the quantitative parameters of EEG in different groups of patients/subjects allows to improve an accuracy of measurement.
authors.affiliation: Laboratory of Neurobiology of Action Programming, Institute of the Human Brain of Russian Academy of Sciences, St. Petersburg, ul. Acad. Pavlova, Russian Federation. veragrin@yahoo.com
authors.forename: Vera A
authors.initials: VA
authors.lastname: Grin-Yatsenko
authors.forename: Ineke
authors.initials: I
authors.lastname: Baas
authors.forename: Valery A
authors.initials: VA
authors.lastname: Ponomarev
authors.forename: Juri D
authors.initials: JD
authors.lastname: Kropotov
mesh_terms: D000293:Adolescent; D000328:Adult; D000513:Alpha Rhythm; D001007:Anxiety / Q000175:diagnosis / Q000503:physiopathology; D001611:Beta Rhythm; D001921:Brain / Q000503:physiopathology*; D001931:Brain Mapping / Q000379:methods; D003627:Data Interpretation, Statistical; D003866:Depressive Disorder / Q000175:diagnosis* / Q000503:physiopathology*; D003937:Diagnosis, Differential; D042241:Early Diagnosis; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials / Q000502:physiology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009778:Occipital Lobe / Q000503:physiopathology; D010296:Parietal Lobe / Q000503:physiopathology; D010775:Photic Stimulation; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D013826:Theta Rhythm; D055815:Young Adult