title: Bayesian estimation of ERP components from multicondition and multichannel EEG.
abstract: 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.
authors: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China; College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China. Electronic address: auweiwu@scut.edu.cn.
authors: Wei
authors: W
authors: Wu
authors: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China. Electronic address: xs.wuchaohua@gmail.com.
authors: Chaohua
authors: C
authors: Wu
authors: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
authors: Shangkai
authors: S
authors: Gao
authors: School of Computer Science and Technology, Tianjin University, Tianjin 300072, China.
authors: Baolin
authors: B
authors: Liu
authors: College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
authors: Yuanqing
authors: Y
authors: Li
authors: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China. Electronic address: gxr-dea@tsinghua.edu.cn.
authors: Xiaorong
authors: X
authors: Gao
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004569:Electroencephalography; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D008297:Male; D055815:Young Adult
keywords: Bayesian estimation; Event-related potentials; Face inversion effect; Latency; N170