title: Learning event-related potentials (ERPs) from multichannel EEG recordings: a spatio-temporal modeling framework with a fast estimation algorithm.
abstract: Extracting event-related potentials (ERPs) from multichannel EEG recordings remains a challenge due to the poor signal-to-noise ratio (SNR). This paper presents a multivariate statistical model of ERPs by exploiting the existing knowledge about their spatio-temporal properties. In particular, a computationally efficient algorithm is derived for fast model estimation. The algorithm, termed SIM, can be intuitively interpreted as maximizing the signal-to-noise ratio in the source space. Using both simulated and real EEG data, we show that the algorithm achieves excellent estimation performance and substantially outperforms a state-of-the-arts algorithm in classification accuracies in a P300 target detection task. The results demonstrate that the proposed modeling framework offers a powerful tool for exploring the spatio-temporal patterns of ERPs as well as learning spatial filters for decoding brain states.
authors.affiliation: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China. weiwu@neurostat.mit.edu
authors.forename: Wei
authors.initials: W
authors.lastname: Wu
authors.forename: Shangkai
authors.initials: S
authors.lastname: Gao
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000473:pathology; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D018913:Event-Related Potentials, P300; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D015233:Models, Statistical; D016011:Normal Distribution; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D013997:Time Factors