Bayesian estimation of ERP components from multicondition and multichannel EEG

• Published in: NeuroImage (Orlando, Fla.) Vol. 88; pp. 319 - 339
• Main Authors: Wu, Wei, Wu, Chaohua, Gao, Shangkai, Liu, Baolin, Li, Yuanqing, Gao, Xiaorong
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.03.2014; Elsevier; Elsevier Limited
• Subjects: Adult; Algorithms; Amplitudes; Approximation; Bayes Theorem; Bayesian analysis; Bayesian estimation; Biological and medical sciences; Brain; Brain - physiology; Data Interpretation, Statistical; Electroencephalography; Enterprise resource planning; Event-related potentials; Evoked Potentials; Face inversion effect; Female; Fundamental and applied biological sciences. Psychology; Humans; Latency; Male; Multichannel; N170; Optimization algorithms; Principal components analysis; Utilities; Vertebrates: nervous system and sense organs; Young Adult; N170; Event-related potentials; Bayesian estimation; Latency; Face inversion effect; Electrophysiology; Electroencephalography; Face; Event evoked potential
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2013.11.028

Extraction and separation of functionally different event-related potentials (ERPs) from electroencephalography (EEG) is a long-standing problem in cognitive neuroscience. In this paper, we propose a Bayesian spatio-temporal model for estimating ERP components from multichannel EEG recorded under multiple experimental conditions. The model isolates the spatially and temporally overlapping ERP components by utilizing their phase-locking structure and the inter-condition non-stationarity structure of their amplitudes and latencies. Critically, unlike in previous multilinear algorithms, the non-phase-locked background EEGs are modeled as spatially correlated and non-isotropic signals. A variational algorithm was developed for approximate Bayesian inference of the proposed model, with the effective number of ERP components automatically determined as a part of the algorithm. The utility of the algorithm is demonstrated with applications to synthetic data and the EEG data collected from 13 subjects during a face inversion experiment. The results show that our algorithm more accurately and reliably estimates the spatio-temporal patterns, amplitudes, and latencies of the underlying ERP components in comparison with several state-of-the-art algorithms. •Isolates ERPs using phase-locking and inter-condition non-stationarity structures•The background EEGs are modeled as spatially correlated and non-isotropic signals.•A variational algorithm was developed for approximate fully Bayesian inference.