Analysis of time-varying coherence of EEG during face recognition based on harmonic transform.

Distinct cortical activity during face recognition was reported by a number of studies. Classical coherence analysis reflects the synchronization between two random processes in certain frequency under the assumption that the signals are stationary. In EEG study, the coherence analysis is mainly used to analyze the coupling and drive-response relation about the brain activities in different regions.

Real-time data-reusing adaptive learning of a radial basis function network for tracking evoked potentials.

Tracking variations in both the latency and amplitude of evoked potential (EP) is important in quantifying properties of the nervous system. Adaptive filtering is a powerful tool for tracking such variations. In this paper, a data-reusing non-linear adaptive filtering method, based on a radial basis function network (RBFN), is implemented to estimate EP.

Adaptive signal enhancement of somatosensory evoked potential for spinal cord compression detection: an experimental study.

The objective of this study was to assess the efficacy of adaptive signal enhancement (ASE) as a means of indicating intraoperative spinal cord impingement. ASE technique was used to determine the changes in the somatosensory evoked potential (SEP) elicited from eighteen rats with varying levels of spinal cord compression. ASE technique was found to be able to effectively extract SEP signals for the detection of spinal cord injury.

Novel sparse component analysis approach to free radical EPR spectra decomposition.

Free radicals play important roles in many physiological and pathological pathways in biological systems. These free radicals can be detected and quantified by their EPR spectra. The measured EPR spectra are often mixtures of pure spectra of several different free radicals and other chemicals.

Multi-adaptive filtering technique for surface somatosensory evoked potentials processing.

Somatosensory evoked potential (SEP) testing has been widely applied to diagnosis of various neurological disorders. However, SEP recorded using surface electrodes is buried in noises, which makes the signal-to-noise ratio (SNR) very poor. Conventional averaging method usually requires up to thousands of raw SEP input trials to increase the SNR so that an identifiable waveform can be produced for latency and amplitude measurement.

Image analysis system for assessment of immunohistochemically stained proliferative marker (MIB-1) in oesophageal squamous cell carcinoma.

The prognosis of oesophageal cancer patients is related to the portion of MIB-1 positively stained tumour nuclei. In this study, an image analysis system was developed based on LEICA Image Processing and Analysis System to reduce the subjective, tedious and inaccurate manual counting of nuclei staining. Representative oesophageal cancer tissues were collected and immunohistochemical preparations of MIB-1 were made.

New Bayesian discriminator for detection of atrial tachyarrhythmias.

Background: Accurate, rapid detection of atrial tachyarrhythmias has important implications in the use of implantable devices for treatment of cardiac arrhythmia. Currently available detection algorithms for atrial tachyarrhythmias, which use the single-index method, have limited sensitivity and specificity.

Methods And Results: In this study, we evaluated the performance of a new Bayesian discriminator algorithm in the detection of atrial fibrillation (AF), atrial flutter (AFL), and sinus rhythm (SR).

Adaptive filtering of evoked potentials with radial-basis-function neural network prefilter.

Evoked potentials (EPs) are time-varying signals typically buried in relatively large background noise. To extract the EP more effectively from noise, we had previously developed an approach using an adaptive signal enhancer (ASE) (Chen et al., 1995).