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A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals

Overview of attention for article published in Frontiers in Neuroscience, January 2013
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Title
A toolbox for real-time subject-independent and subject-dependent classification of brain states from fMRI signals
Published in
Frontiers in Neuroscience, January 2013
DOI 10.3389/fnins.2013.00170
Pubmed ID
Authors

Mohit Rana, Nalin Gupta, Josue L. Dalboni Da Rocha, Sangkyun Lee, Ranganatha Sitaram

Abstract

There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBSVM, and SVMlight packages to achieve a cross-platform environment. MANAS is targeted for neuroscience investigations and brain rehabilitation applications, based on neurofeedback and brain-computer interface (BCI) paradigms. MANAS provides two different approaches for real-time classification: subject dependent and subject independent classification. In this article, we present the methodology of real-time subject dependent and subject independent pattern classification of fMRI signals; the MANAS software architecture and subsystems; and finally demonstrate the use of the system with experimental results.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Japan 2 2%
Nigeria 1 1%
Italy 1 1%
United Kingdom 1 1%
China 1 1%
Unknown 81 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 24%
Researcher 20 22%
Professor > Associate Professor 11 12%
Student > Master 8 9%
Student > Bachelor 6 7%
Other 16 18%
Unknown 7 8%
Readers by discipline Count As %
Neuroscience 20 22%
Psychology 19 21%
Engineering 11 12%
Computer Science 8 9%
Medicine and Dentistry 8 9%
Other 13 14%
Unknown 11 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 October 2013.
All research outputs
#19,944,091
of 25,373,627 outputs
Outputs from Frontiers in Neuroscience
#8,668
of 11,538 outputs
Outputs of similar age
#221,299
of 288,991 outputs
Outputs of similar age from Frontiers in Neuroscience
#169
of 246 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,538 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 288,991 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 246 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.