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Randal Burns,
Kunal Lillaney,
Daniel R. Berger,
Logan Grosenick,
Karl Deisseroth,
R. Clay Reid,
William Gray Roncal,
Priya Manavalan,
Davi D. Bock,
Narayanan Kasthuri,
Michael Kazhdan,
Stephen J. Smith,
Dean Kleissas,
Eric Perlman,
Kwanghun Chung,
Nicholas C. Weiler,
Jeff Lichtman,
Alexander S. Szalay,
Joshua T. Vogelstein,
R. Jacob Vogelstein
July 2013
SSDBM: Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Publisher: ACM
Bibliometrics:
Citation Count: 1
Downloads (6 Weeks): 4, Downloads (12 Months): 41, Downloads (Overall): 250
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We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes ---neural connectivity maps of the brain---using the parallel ...
Keywords:
connectomics, data-intensive computing
2
June 2009
ISBI'09: Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Publisher: IEEE Press
Advances in microscopy and biochemistry now allow investigators to image the calcium dynamics of hundreds to thousands of neurons in awake behaving animals. However, as speed and resolution of such techniques rapidly increase, so do the dimension and complexity of the data collected. ICA has been widely employed to reveal ...
Keywords:
in vivo, ICA, neuroimaging, calcium imaging, sparse regression
3
June 2009
ISBI'09: Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Publisher: IEEE Press
We studied the performance of a double-spatial filtering method for classification of single-trial electroencephalography (EEG) data that couples the spherical surface Laplacian (SL) and independent component analysis (ICA). This method was evaluated in the context of a binary classification experiment with brain states driven by mental imagery of auditory and ...
Keywords:
ICA, surface Laplacian, EEG classification, BCI
4
May 2006
ISNN'06: Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Publisher: Springer-Verlag
We have previously shown that classification of single-trial electroencephalographic (EEG) recordings is improved by the use of either a multichannel classifier or the best independent component over a single channel classifier. In this paper, we introduce a classifier that makes explicit use of multiple independent components. Two models are compared. ...
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