Biomedical subjects

Hualou Liang

Publications and source records attributed to Hualou Liang.

8 recordsLinked to original sources

PubMed2006

Single-trial evoked potential estimation using wavelets.

In this paper we present conventional and translation-invariant (TI) wavelet-based approaches for single-trial evoked potential estimation based on intracortical recordings. We demonstrate that the wavelet-based approaches outperform several existing methods including the Wiener filter, least mean square (LMS), and recursive least squares (RLS), and that the TI wavelet-based estimates have higher SNR and lower RMSE than the conventional wavelet-based estimates. We also show that multichannel averaging significantly improves the evoked potential estimation, especially for the wavelet-based approaches. The excellent performances of the wavelet-based approaches for extracting evoked potentials are demonstrated via examples using simulated and experimental data.

Algorithms↗

PubMed2006

Relaxation-based multichannel signal combination (RELAX-MUSIC) for ROC analysis of percept-related neuronal activity.

In this letter, we consider how to combine neuronal signals from multiple electrodes to optimally predict behavioral choices from observed neural activity. The predictability is often quantified by the area under the receiver operating characteristic (ROC) curve, also called choice probability (CP) in neurophysiology. We exploit a distribution-free relaxation based multichannel signal combination (RELAX-MUSIC) approach that requires only simple pairwise combination and recursive implementation for optimizing the area under the ROC curve. A permutation test is employed to assess the statistical significance of the derived CP. We demonstrate that the RELAX-MUSIC approach outperforms the commonly used response pooling and Fisher linear discriminant (FLD) methods. The excellent performances of the RELAX-MUSIC approach for predicting perceptual decisions from neural activity are demonstrated via examples using simulated and experimental data.

Action Potentials↗

PubMed2005

Empirical mode decomposition of field potentials from macaque V4 in visual spatial attention.

Empirical mode decomposition (EMD) has recently been introduced as a local and fully data-driven technique for the analysis of non-stationary time-series. It allows the frequency and amplitude of a time-series to be evaluated with excellent time resolution. In this article we consider the application of EMD to the analysis of neuronal activity in visual cortical area V4 of a macaque monkey performing a visual spatial attention task. We show that, by virtue of EMD, field potentials can be resolved into a sum of intrinsic components with different degrees of oscillatory content. Low-frequency components in single-trial recordings contribute to the average visual evoked potential (AVEP), whereas high-frequency components do not, but are identified as gamma-band (30-90 Hz) oscillations. The magnitude of time-varying gamma activity is shown to be enhanced when the monkey attends to a visual stimulus as compared to when it is not attending to the same stimulus. Comparison with Fourier analysis shows that EMD may offer better temporal and frequency resolution. These results support the idea that the magnitude of gamma activity reflects the modulation of V4 neurons by visual spatial attention. EMD, coupled with instantaneous frequency analysis, is demonstrated to be a useful technique for the analysis of neurobiological time-series.

Algorithms↗

PubMed2005

Assessment of the esophageal pressure in gastroesophageal reflux disease by the local regression.

Gastroesophageal reflux disease (GERD) is one of the most prevalent gastrointestinal diseases. It is characterized by excessive reflux of gastric content (acid, pepsin, etc.) into the esophagus causing symptoms (heartburn, acid regurgitation, etc.) and mucosal inflammation and injuries. GERD occurs when the lower esophageal sphincter (LES) has a low resting pressure and stomach contents leak back, or reflux, into the esophagus. Therefore, the accurate measurement of the LES pressure is of great importance for the diagnosis of GERD. The LES pressure signal, involving severe respiratory contamination and motion artifacts, demands specific capabilities not provided by conventional data analysis methods. Recently, local regression has proved to be a very attractive technique to the nonparametric regression in statistics. In this contribution we apply the ideas of local regression to develop strategies for selecting smoothing parameters of local linear squares estimators, and present its application on the extraction of the LES pressure in GERD. The results from both extensive simulations and real data demonstrate the ability of local regression to characterize the LES pressure, which is consistent with the clinical observation.

Computer Simulation↗

PubMed2005

Application of the empirical mode decomposition to the analysis of esophageal manometric data in gastroesophageal reflux disease.

The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and nonstationary time series. The central idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode functions (IMFs). An IMF is defined as any function having the number of extrema and the number of zero-crossings equal (or differing at most by one), and also having symmetric envelopes defined by the local minima, and maxima respectively. The decomposition procedure is adaptive, data-driven, therefore, highly efficient. In this contribution, we applied the idea of EMD to develop strategies to automatically identify the relevant IMFs that contribute to the slow-varying trend in the data, and presented its application on the analysis of esophageal manometric time series in gastroesophageal reflux disease. The results from both extensive simulations and real data show that the EMD may prove to be a vital technique for the analysis of esophageal manometric data.

Algorithms↗

PubMed2005

Causal influence: advances in neurosignal analysis.

The analysis of multichannel recordings such as electroencephalography (EEG) and magnetoencephalography (MEG) is important both for basic brain research and for medical diagnosis and treatment. Multivariate linear regressive analysis such as the AutoRegressive (MAR) modeling is an effective means to characterize, with high spatial, temporal, and frequency resolution, functional relations within multichannel neuronal data. Recent advances in MAR modeling show promise for the analysis and visualization of large-scale network interactions, especially in the ability to assess their causal relations. This article provides a detailed review of the advances in the development and application of causal influence measures for analyzing neurosignal within the framework of the MAR spectral analysis. First, we outline mathematical formulations of the MAR model and its related estimation procedures, with emphasis on the development of causal influence measures for analyzing brain circuits. Second, we address the technical issues on the practical applications of the causal measures to the neurobiological data. Of particular interest is the recent development of adapting the MAR to analyze neural spike train data. Third, we present a variety of applications ranging from basic neuroscience research to clinical applications as well as functional neuroimaging. We finally conclude with a brief summary and discuss future research development in this field.

Action Potentials↗

PubMed2002

Synchronized activity in prefrontal cortex during anticipation of visuomotor processing.

It is commonly presumed, though not well established, that the prefrontal cortex exerts top-down control of sensory processing. One aspect of this control is thought to be a facilitation of sensory pathways in anticipation of such processing. To investigate the possible involvement of prefrontal cortex in anticipatory top-down control, we studied the statistical relations between prefrontal activity, recorded while a macaque monkey waited for presentation of a visual stimulus, and subsequent sensory and motor events. Local field potentials were simultaneously recorded from prefrontal, motor, occipital and temporal cortical sites in the left cerebral hemisphere. Spectral power and coherence analysis revealed that during stimulus anticipation three of five prefrontal sites participated in a coherent oscillatory network synchronized in the beta-frequency range. Pre-stimulus network power and coherence were highly correlated with the amplitude and latency of early visual evoked potential components in visual cortical areas, and with response time. The results suggest that synchronized oscillatory networks in prefrontal cortex are involved in top-down anticipatory mechanisms that facilitate subsequent sensory processing in visual cortex. They further imply that stronger top-down control leads to larger and faster sensory responses, and a subsequently faster motor response.

Animals↗

PubMed2002

Stimulus artifact cancellation in the serosal recordings of gastric myoelectric activity using wavelet transform.

Previous studies have shown that electrical stimulation of the stomach (i.e., gastric pacing) with appropriate parameters is a promising method for treatment of gastroparetic patients. The recording of gastric myoelectric activity (GMA) by serosal electrodes is often used to evaluate the effect of stimulation. However, the major problem with the measurement of GMA during gastric pacing is the stimulus artifacts which are often superimposed on the serosal recording and make analysis difficult. The frequency-domain adaptive filter has been used to reduce the stimulus artifacts but only with limited success. This paper describes a wavelet transform-based method for the reduction of stimulus artifacts in the serosal recordings of GMA. The key of this method lies in the use of the fuzzy set theory to select the stimulus artifact-related modulus maxima in the wavelet domain. Both quantitative and qualitative measures show that significant stimulus artifact cancellation was achieved through a series of computer simulations. Results from both single- and multichannel serosally recorded myoelectric signals during gastric pacing are presented to demonstrate the efficiency of the proposed method for the cancellation of stimulus artifacts.

Algorithms↗