Bayesian estimation of ERP components from multicondition and multichannel EEG

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• 作者：Wei Wu ; Chaohua Wu ; Shangkai Gao ; Baolin Liu ; Yuanqing Li ; Xiaorong Gao
• 关键词：Event-related potentials ; Latency ; Face inversion effect ; N170 ; Bayesian estimation
• 刊名：NeuroImage
• 出版年：March, 2014
• 年：2014
• 卷：88
• 期：Complete
• 页码：319-339
• 全文大小：2836 K

文摘

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.