Download - Laskaris mining information_neuroinformatics
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N. LaskarisN. Laskaris
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N. LaskarisN. Laskaris
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[ IEEE SP Magazine, May 2004 ]
N. Laskaris,S. Fotopoulos,
A. Ioannides
ENTER-2001
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new toolsnew toolsfor Mining Information from for Mining Information from
multichannel encephalographic recordingsmultichannel encephalographic recordings
& applications& applications
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What is Data Mining ?Data Mining ?
How is it applied ?
Why is it useful ?What is the difficulty with single trials ?
How can Data MiningData Mining help ?
Which are the algorithmic steps ?
Is there a simple example ?
Is there a more elaborate example ?
What has been the gain ?
Where one can learn more ?
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What is Data MiningData Mining&
Knowledge DiscoveryKnowledge Discovery in databases ?
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Data MiningData Miningis “the data-driven discovery and
modeling of hidden patterns in large volumes of data.”
It is a multidisciplinary field, borrowing and enhancing ideas from diverse areas such as statistics, image understanding, mathematical optimization, computer vision, and pattern recognition.
It is the process of nontrivialnontrivial extractionextraction of implicitof implicit, previously unknown, and
potentially useful information from voluminous datafrom voluminous data.
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How is it applied in the context of
multichannel multichannel encephalographic recordingsencephalographic recordings ?
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Studying Brain’s self-organizationby monitoring the dynamic pattern formation
reflecting neural activity
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Why is it a potentially valuable methodology
for analyzing EventEvent--RelatedRelated recordings ?
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The analysis of EventEvent--Related DynamicsRelated Dynamics
aims at understanding the realreal--time processingtime processing of a stimulus
performed in the cortex
and demands tools able to deal with MultiMulti--Trial dataTrial data
The traditional approach is based on identifying peaks in the averaged signal
-It blends everything happened during
the recording
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What is the difficulty in analyzing
SingleSingle--TrialTrial responses ?
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At the single-trial level, we are facing ComplexComplex SpatiotemporalSpatiotemporal DynamicsDynamics
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How can Data MiningData Mining help to circumvent this complexity
and reveal the underlying brain mechanisms ?
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directed queries are formed in the Single-Trial data
which are then summarized
using a very limited vocabulary of information granules
that are easily understood, accompanied by well-defined semanticsand help express relationships existing in the data
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The information abstractioninformation abstractionis usually accomplished
via clusteringclustering techniques
and followed by a proper visualization schemevisualization schemethat can readily spot interesting events
and trends in the experimental data.
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- Semantic MapsSemantic Maps
The CartographyCartography of neural function results in a topographical representation
of response variation and enables the virtual navigationin the encephalographic database
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Which are the intermediate
algorithmic steps ?
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A Hybrid approach A Hybrid approach
Pattern AnalysisPattern Analysis& Graph TheoryGraph Theory
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Step_Step_the spatiotemporal dynamics are decomposed
Design of the spatial filterspatial filter used to extract
the temporal patternstemporal patterns conveyingthe regional response dynamics
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Step_Step_Pattern AnalysisPattern Analysisof the extracted ST-patterns
Interactive Study Interactive Study of pattern variabilityof pattern variability
Feature Feature extractionextraction
Embedding Embedding in Feature Spacein Feature Space
Clustering & Clustering & Vector QuantizationVector Quantization
Minimal Spanning TreeMinimal Spanning Treeof the codebookof the codebook
MSTMST--ordering ordering of the code vectors of the code vectors
Orderly presentation Orderly presentation of response variabilityof response variability
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Step_Step_Within-group Analysisof regional response dynamics
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Step_Step_Within-group Analysisof multichannel single-trial signals
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Step_Step_Within-group Analysis of single-trial MFTMFT--solutionssolutions
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Is there a simple example?
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[ Laskaris & Ioannides, Clin. Neurophys., 2001 ]
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Repeated stimulationRepeated stimulation120 trials, binaural-stimulation[ 1kHz tones, 0.2s, 45 dB ], ISI: 3sec, passive listening
Task : to ‘‘explain’’the averaged M100-response
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The M100-peak emerges from the stimulus-induced phase-resetting
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Phase reorganizationPhase reorganizationof the ongoing brain waves
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Is there a more elaborate example?
[ Laskaris et al., NeuroImage, 2003 ]
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A study of A study of global firing patternsglobal firing patterns
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Their relation Their relation with localized sources with localized sources
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and and ……..initiating eventsinitiating events
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240 trials, pattern reversal, 4.5 deg , ISI: 0.7 sec, passive viewing
Single-Trial data in unorganized format
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Single-Trial data summarized via ordered prototypes reflecting the variability of regional response dynamics
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‘‘‘‘The ongoing activity The ongoing activity before the stimulusbefore the stimulus--onsetonsetis functionally coupled is functionally coupled with the subsequent with the subsequent regional responseregional response’’’’
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Polymodal Parietal Areas BA5 & BA7 are the major sources of the observed variability
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There is relationship There is relationship between
N70m between
N70m--response variability response variability
and activity in early visual areas.
and activity in early visual areas.
Regional vs Local response dynamics :
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What has been the lesson, so far,
from the analysis of Event-Related Dynamics ?
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The The ‘‘‘‘dangerousdangerous’’’’equation equation
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Where one can learn more about Mining InformationMining Information
from encephalographic recordings ?
////www.hbd.brain.riken.jp www.hbd.brain.riken.jp symposium: MEG in psychiatrysymposium: MEG in psychiatry
(AUD4, Monday, 9.00 )(AUD4, Monday, 9.00 )
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[email protected]@zeus.csd.auth.gr