Spatiospectral Decomposition of Multi-subject EEG: Evaluating Blind Source Separation Algorithms on Real and Realistic Simulated Data

• Published in: Brain topography Vol. 31; no. 1; pp. 47 - 61
• Main Authors: Bridwell, David A., Rachakonda, Srinivas, Silva, Rogers F., Pearlson, Godfrey D., Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2018; Springer Nature B.V
• Subjects: Adult; Aged; Algorithms; Biomedical and Life Sciences; Biomedicine; Blindness; Brain Mapping; Cognition - physiology; Cognitive ability; Computer programs; Computer Simulation; Decomposition; EEG; Electroencephalography; Electroencephalography - methods; Electroencephalography - statistics & numerical data; Feasibility Studies; Humans; Male; Middle Aged; Models, Neurological; Nerve Net - anatomy & histology; Nerve Net - physiology; Neurology; Neurosciences; Original Paper; Oscillations; Psychiatry; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; Software; Wavelet Analysis; Young Adult; Simulated EEG; Wavelets; Multi-subject decomposition; Blind source separation; Resting EEG; ICA

Electroencephalographic (EEG) oscillations predominantly appear with periods between 1 s (1 Hz) and 20 ms (50 Hz), and are subdivided into distinct frequency bands which appear to correspond to distinct cognitive processes. A variety of blind source separation (BSS) approaches have been developed an...