H
Hualou Liang
Researcher at Drexel University
Publications - 102
Citations - 3868
Hualou Liang is an academic researcher from Drexel University. The author has contributed to research in topics: Visual perception & Visual cortex. The author has an hindex of 27, co-authored 96 publications receiving 3406 citations. Previous affiliations of Hualou Liang include Center for Complex Systems and Brain Sciences & Chinese Academy of Sciences.
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Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment.
TL;DR: It is shown that with proper data preprocessing, Adaptive MultiVariate AutoRegressive (AMVAR) modeling is an effective technique for dealing with nonstationary ERP time series and a bootstrap procedure is proposed to assess the variability in the estimated spectral quantities.
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A backward progression of attentional effects in the ventral stream
TL;DR: The magnitude and latency of attentional enhancement of firing rates in V1, V2, and V4 in the same animals performing the same task are compared to suggest that attentional mechanisms operate via feedback from higher-order areas to lower-order ones.
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Empirical mode decomposition: a method for analyzing neural data
TL;DR: The use of the data-driven empirical mode decomposition (EMD) method to study neuronal activity in visual cortical area V4 of macaque monkeys performing a visual spatial attention task found that local field potentials were resolved by the EMD into the sum of a set of intrinsic components with different degrees of oscillatory content.
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2008 Special Issue: BSMART: A Matlab/C toolbox for analysis of multichannel neural time series
TL;DR: A Matlab/C toolbox, Brain-SMART (System for Multivariate AutoRegressive Time series, or BSMART), for spectral analysis of continuous neural time series data recorded simultaneously from multiple sensors designed for easy accessibility.
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Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease
TL;DR: The idea of EMD was applied to develop strategies to automatically identify the relevant IMFs that contribute to the slow-varying trend in the data, and its application on the analysis of esophageal manometric time series in gastroesophagal reflux disease was presented.