Xiaomu Song

413 total citations
33 papers, 294 citations indexed

About

Xiaomu Song is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Xiaomu Song has authored 33 papers receiving a total of 294 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Cognitive Neuroscience, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Signal Processing. Recurrent topics in Xiaomu Song's work include EEG and Brain-Computer Interfaces (13 papers), Neural dynamics and brain function (10 papers) and Functional Brain Connectivity Studies (9 papers). Xiaomu Song is often cited by papers focused on EEG and Brain-Computer Interfaces (13 papers), Neural dynamics and brain function (10 papers) and Functional Brain Connectivity Studies (9 papers). Xiaomu Song collaborates with scholars based in United States. Xiaomu Song's co-authors include Guoliang Fan, Alice M. Wyrwicz, Nan‐kuei Chen, Mahesh Rao, Lawrence P. Panych, Matthew C. Murphy, Michael J. Miller, Guoliang Fan, Craig Weiss and John F. Disterhoft and has published in prestigious journals such as Journal of Neuroscience, NeuroImage and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Xiaomu Song

32 papers receiving 282 citations

Peers

Xiaomu Song
Atsunori Kanemura Japan
Yunbo Tang China
Ozan Özdenizci United States
Hengjin Ke China
Weijia Feng China
Howida A. Shedeed Egypt
David J. Anderson United States
Foad Ghaderi Iran
Atsunori Kanemura Japan
Xiaomu Song
Citations per year, relative to Xiaomu Song Xiaomu Song (= 1×) peers Atsunori Kanemura

Countries citing papers authored by Xiaomu Song

Since Specialization
Citations

This map shows the geographic impact of Xiaomu Song's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xiaomu Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaomu Song more than expected).

Fields of papers citing papers by Xiaomu Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaomu Song. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xiaomu Song. The network helps show where Xiaomu Song may publish in the future.

Co-authorship network of co-authors of Xiaomu Song

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaomu Song. A scholar is included among the top collaborators of Xiaomu Song based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xiaomu Song. Xiaomu Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Song, Xiaomu, Lawrence P. Panych, & Nan‐kuei Chen. (2015). Data-Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI Reproducibility. Brain Connectivity. 6(2). 136–151. 22 indexed citations
2.
Song, Xiaomu, Lawrence P. Panych, & Nan‐kuei Chen. (2015). Spatially regularized machine learning for task and resting-state fMRI. Journal of Neuroscience Methods. 257. 214–228. 1 indexed citations
3.
Song, Xiaomu, et al.. (2015). Improving brain–computer interface classification using adaptive common spatial patterns. Computers in Biology and Medicine. 61. 150–160. 46 indexed citations
4.
Song, Xiaomu & Nan‐kuei Chen. (2014). A SVM-based quantitative fMRI method for resting-state functional network detection. Magnetic Resonance Imaging. 32(7). 819–831. 12 indexed citations
5.
Song, Xiaomu & Nan‐kuei Chen. (2013). Resting state fMRI data analysis using support vector machines. 1–6. 1 indexed citations
6.
Song, Xiaomu, Nan‐kuei Chen, & Pooja Gaur. (2013). A kernel machine-based fMRI physiological noise removal method. Magnetic Resonance Imaging. 32(2). 150–162. 2 indexed citations
7.
Song, Xiaomu, et al.. (2013). Adaptive Common Spatial Pattern for single-trial EEG classification in multisubject BCI. 411–414. 19 indexed citations
8.
Song, Xiaomu, et al.. (2012). A study of kernel CSP-based motor imagery brain computer interface classification. 1–4. 12 indexed citations
9.
Song, Xiaomu, Limin Li, Daniil P. Aksenov, Michael J. Miller, & Alice M. Wyrwicz. (2010). Mapping rabbit whisker barrels using discriminant analysis of high field fMRI data. NeuroImage. 51(2). 775–782. 4 indexed citations
10.
Song, Xiaomu & Alice M. Wyrwicz. (2009). Unsupervised spatiotemporal fMRI data analysis using support vector machines. NeuroImage. 47(1). 204–212. 32 indexed citations
11.
Miller, Michael J., et al.. (2008). Functional Magnetic Resonance Imaging of Delay and Trace Eyeblink Conditioning in the Primary Visual Cortex of the Rabbit. Journal of Neuroscience. 28(19). 4974–4981. 19 indexed citations
12.
Song, Xiaomu, Guoliang Fan, & Mahesh Rao. (2008). SVM-Based Data Editing for Enhanced One-Class Classification of Remotely Sensed Imagery. IEEE Geoscience and Remote Sensing Letters. 5(2). 189–193. 21 indexed citations
13.
Song, Xiaomu & Guoliang Fan. (2007). Selecting Salient Frames for Spatiotemporal Video Modeling and Segmentation. IEEE Transactions on Image Processing. 16(12). 3035–3046. 9 indexed citations
14.
Song, Xiaomu, et al.. (2007). One-class Machine Learning for Brain Activation Detection. 23. 1–6. 3 indexed citations
15.
Song, Xiaomu, Matthew C. Murphy, & Alice M. Wyrwicz. (2006). Spatiotemporal Denoising and Clustering of fMRI Data. 2857–2860. 13 indexed citations
16.
Song, Xiaomu & Guoliang Fan. (2005). Joint Key-Frame Extraction and Object-Based Video Segmentation. 126–131. 9 indexed citations
17.
18.
Song, Xiaomu & Guoliang Fan. (2004). A study of supervised, semi-supervised and unsupervised multiscale Bayesian image segmentation. 2. II–371. 4 indexed citations
19.
Song, Xiaomu & Guoliang Fan. (2003). Unsupervised image segmentation using wavelet-domain hidden Markov models. 3 indexed citations
20.
Liu, Lijie, et al.. (2003). A simplified quantization rate-distortion model for fast document image segmentation. 2. II–557. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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