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Detecting alpha spindle events in EEG time series using adaptive autoregressive models

Overview of attention for article published in BMC Neuroscience, September 2013
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Title
Detecting alpha spindle events in EEG time series using adaptive autoregressive models
Published in
BMC Neuroscience, September 2013
DOI 10.1186/1471-2202-14-101
Pubmed ID
Authors

Vernon Lawhern, Scott Kerick, Kay A Robbins

Abstract

Rhythmic oscillatory activity is widely observed during a variety of subject behaviors and is believed to play a central role in information processing and control. A classic example of rhythmic activity is alpha spindles, which consist of short (0.5-2 s) bursts of high frequency alpha activity. Recent research has shown that alpha spindles in the parietal/occipital area are statistically related to fatigue and drowsiness. These spindles constitute sharp changes in the underlying statistical properties of the signal. Our hypothesis is that change point detection models can be used to identify the onset and duration of spindles in EEG. In this work we develop an algorithm that accurately identifies sudden bursts of narrowband oscillatory activity in EEG using techniques derived from change point analysis. Our motivating example is detection of alpha spindles in the parietal/occipital areas of the brain. Our goal is to develop an algorithm that can be applied to any type of rhythmic oscillatory activity of interest for accurate online detection.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Portugal 1 1%
Russia 1 1%
Italy 1 1%
Unknown 87 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 31%
Researcher 20 22%
Student > Master 11 12%
Student > Bachelor 7 8%
Student > Postgraduate 5 5%
Other 12 13%
Unknown 8 9%
Readers by discipline Count As %
Engineering 29 32%
Neuroscience 12 13%
Computer Science 12 13%
Psychology 9 10%
Agricultural and Biological Sciences 5 5%
Other 10 11%
Unknown 14 15%
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 18 September 2013.
All research outputs
#20,202,510
of 22,721,584 outputs
Outputs from BMC Neuroscience
#1,052
of 1,241 outputs
Outputs of similar age
#176,759
of 201,958 outputs
Outputs of similar age from BMC Neuroscience
#30
of 47 outputs
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