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	<title>Neurobot &#187; Wavelets</title>
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	<link>http://neurobot.bio.auth.gr</link>
	<description>A computational neuroscience and neuroinformatics blog</description>
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		<title>ButIf Toolbox</title>
		<link>http://neurobot.bio.auth.gr/2009/butif-toolbox/</link>
		<comments>http://neurobot.bio.auth.gr/2009/butif-toolbox/#comments</comments>
		<pubDate>Thu, 14 May 2009 09:30:03 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Brain Research]]></category>
		<category><![CDATA[Wavelets]]></category>

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		<description><![CDATA[BUTIFtoolbox is used to extract transient oscillatory dynamics from signals. Until now, it was succesfully applied to LFP (Local Field Potentials) and EEG (Electroencephalographic) signals. Applications to other fields could be researched.]]></description>
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		<title>An Introduction to Wavelets</title>
		<link>http://neurobot.bio.auth.gr/2006/an-introduction-to-wavelets/</link>
		<comments>http://neurobot.bio.auth.gr/2006/an-introduction-to-wavelets/#comments</comments>
		<pubDate>Tue, 05 Sep 2006 16:27:50 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Wavelets]]></category>

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		<description><![CDATA[&#8220;Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. They have advantages over traditional Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes.&#8221; This paper introduces wavelets to the interested technical person outside of the [...]]]></description>
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		<title>The Wavelet Tutorial: The Engineer&#8217;s Ultimate Guide to Wavelet Analysis</title>
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		<comments>http://neurobot.bio.auth.gr/2006/the-wavelet-tutorial-the-engineers-ultimate-guide-to-wavelet-analysis/#comments</comments>
		<pubDate>Fri, 18 Aug 2006 07:10:03 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Tutorials]]></category>
		<category><![CDATA[Wavelets]]></category>

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		<description><![CDATA[An excellent tutorial on Wavelet Analysis covering also the basic concepts of mathematical transformations, time-frequency representations and non-stationary signal properties. By Robi Polikar Dept. of Electrical and Computer Engineering Rowan University Part I of this tutorial presents an overview of the basic concepts that are of importance in understanding the wavelet theory. This part summarizes [...]]]></description>
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		<title>Automatic spike detection and sorting using wavelets and super-paramagnetic clustering</title>
		<link>http://neurobot.bio.auth.gr/2006/automatic-spike-detection-and-sorting-using-wavelets-and-super-paramagnetic-clustering/</link>
		<comments>http://neurobot.bio.auth.gr/2006/automatic-spike-detection-and-sorting-using-wavelets-and-super-paramagnetic-clustering/#comments</comments>
		<pubDate>Wed, 02 Aug 2006 20:42:29 +0000</pubDate>
		<dc:creator>Dimitrios A. Adamos</dc:creator>
				<category><![CDATA[Software]]></category>
		<category><![CDATA[Clustering]]></category>
		<category><![CDATA[Spike Sorting]]></category>
		<category><![CDATA[Wavelets]]></category>

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		<description><![CDATA[Wave_clus is a fast and unsupervised algorithm for spike detection and sorting. Although it gives a first unsupervised solution, this can be further modified according to the experimenter&#8217;s preference (semi-automatic sorting). By Rodrigo Quian Quiroga, Reader in Bioengineering, Dept. Engineering. University of Leicester, UK. The method combines the wavelet transform,which localizes distinctive spike features, with [...]]]></description>
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