A combined cICA-EEMD analysis of EEG recordings from depressed or schizophrenic patients during olfactory stimulation. (19th December 2016)

• Record Type: Journal Article
• Authors: Götz, Th; Stadler, L; Fraunhofer, G; Tomé, A M; Hausner, H; Lang, E W
• Is Part Of: Journal of neural engineering. Volume 14:Number 1(2017:Feb.)
• Publication Date: 2016-12-19
• Subjects: constraint independent component analysis -- ensemble empirical mode decomposition -- independent modes -- electroencephalography -- depression -- schizophrenia; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/14/1/016011
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . We propose a combination of a constrained independent component analysis (cICA) with an ensemble empirical mode decomposition (EEMD) to analyze electroencephalographic recordings from depressed or schizophrenic subjects during olfactory stimulation. Approach . EEMD serves to extract intrinsic modes (IMFs) underlying the recorded EEG time. The latter then serve as reference signals to extract the most similar underlying independent component within a constrained ICA. The extracted modes are further analyzed considering their power spectra. Main results . The analysis of the extracted modes reveals clear differences in the related power spectra between the disease characteristics of depressed and schizophrenic patients. Such differences appear in the high frequency γ-band in the intrinsic modes, but also in much more detail in the low frequency range in the α-, θ- and δ-bands. Significance . The proposed method provides various means to discriminate both disease pictures in a clinical environment.