Biomedical subjects

Changshui Zhang

Publications and source records attributed to Changshui Zhang.

4 recordsLinked to original sources

PubMed2006

Adaptive feature extraction for EEG signal classification.

One challenge in the current research of brain-computer interfaces (BCIs) is how to classify time-varying electroencephalographic (EEG) signals as accurately as possible. In this paper, we address this problem from the aspect of updating feature extractors and propose an adaptive feature extractor, namely adaptive common spatial patterns (ACSP). Through the weighed update of signal covariances, the most discriminative features related to the current brain states are extracted by the method of multi-class common spatial patterns (CSP). Pseudo-online simulations of EEG signal classification with a support vector machine (SVM) classifier for multi-class mental imagery tasks show the effectiveness of the proposed adaptive feature extractor.

Algorithms↗

PubMed2006

Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs.

Canonical correlation analysis (CCA) is applied to analyze the frequency components of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG). The essence of this method is to extract a narrowband frequency component of SSVEP in EEG. A recognition approach is proposed based on the extracted frequency features for an SSVEP-based brain computer interface (BCI). Recognition Results of the approach were higher than those using a widely used fast Fourier transform (FFT)-based spectrum estimation method.

Algorithms↗

PubMed2006

Generalized manifold-ranking-based image retrieval.

In this paper, we propose a general transductive learning framework named generalized manifold-ranking-based image retrieval (gMRBIR) for image retrieval. Comparing with an existing transductive learning method named MRBIR [12], our method could work well whether or not the query image is in the database; thus, it is more applicable for real applications. Given a query image, gMRBIR first initializes a pseudo seed vector based on neighborhood relationship and then spread its scores via manifold ranking to all the unlabeled images in the database. Furthermore, in gMRBIR, we also make use of relevance feedback and active learning to refine the retrieval result so that it converges to the query concept as fast as possible. Systematic experiments on a general-purpose image database consisting of 5,000 Corel images demonstrate the superiority of gMRBIR over state-of-the-art techniques.

Algorithms↗

PubMed2004

Weighted competition scale-free network.

While many scale-free (SF) networks have been introduced recently for complex systems, most of them are binary random graphs and the rate at which the node in the network increases its connectivity depends on the time it arrived. We propose a model of weighted scale-free networks incorporating a fit-gets-richer scheme which means the connectivity of the node depends on both the degree and fitness of the node. The topology and weights of links of the network evolve as time goes on. The combined numerical and analytical approach indicates that asymptotically the scaling behaviors of the total weight distribution and the connectivity distribution are identical. The asymptotical sameness has also been observed in real networks.

Journal Article↗