Laskaris mining information_neuroinformatics

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Mining Information from Multichannel Encephalographic data

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Laskaris mining information_neuroinformatics

  1. 1. N. Laskaris
  2. 2. N. Laskaris
  3. 3. [ IEEE SP Magazine, May 2004 ] N. Laskaris, S. Fotopoulos, A. IoannidesENTER-2001
  4. 4. new tools for Mining Information frommultichannel encephalographic recordings & applications
  5. 5. What is Data Mining ? How is it applied ? Why is it useful ?What is the difficulty with single trials ? How can Data 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 ?
  6. 6. What is Data Mining &Knowledge Discovery in databases ?
  7. 7. Data Mining is “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 suchas statistics, image understanding, mathematicaloptimization, computer vision, and pattern recognition. It is the process of nontrivial extraction of implicit, implicit previously unknown, andpotentially useful information from voluminous data. data
  8. 8. How is it applied in the context of multichannelencephalographic recordings ?
  9. 9. Studying Brain’s self-organizationby monitoring the dynamic pattern formation reflecting neural activity
  10. 10. 3 Why is ita potentially valuable methodology for analyzing Event-Related recordings ?
  11. 11. The traditional -It blends everything approach is based on happened during identifying peaks the recording in the averaged signal The analysis of Event-Related Dynamics aims at understandingthe real-time processing of a stimulus performed in the cortex and demands tools able to deal with Multi-Trial data
  12. 12. 4 What is the difficulty in analyzingSingle-Trial responses ?
  13. 13. At the single-trial level,we are facingComplex Spatiotemporal Dynamics
  14. 14. 5 How can Data Mining help to circumvent this complexity and revealthe underlying brain mechanisms ?
  15. 15. directed queries are formed in the Single-Trial data which are then summarizedusing a very limited vocabulary of information granulesthat are easily understood,accompanied by well-defined semanticsand help express relationships existing in the data
  16. 16. The information abstraction is usually accomplished via clustering techniquesand followed by a proper visualization scheme that can readily spot interesting events and trends in the experimental data.
  17. 17. - Semantic Maps The Cartography of neural function results in a topographical representation of response variation and enables the virtual navigation in the encephalographic database
  18. 18. 6 Which are the intermediate algorithmic steps ?
  19. 19. A Hybrid approachPattern Analysis & Graph Theory
  20. 20. Step_the spatiotemporal dynamics are decomposed Design of the spatial filter used to extract the temporal patterns conveying the regional response dynamics
  21. 21. Step_Pattern Analysisof the extracted ST-patterns Clustering & Vector Quantization Feature Embedding extraction in Feature Space Minimal Spanning Tree of the codebook Interactive Study of pattern variability Orderly presentation of response variability MST-ordering of the code vectors
  22. 22. Step_Within-group Analysisof regional response dynamics -
  23. 23. Step_Within-group Analysisof multichannel single-trial signals
  24. 24. Step_Within-group Analysisof single-trial MFT-solutions
  25. 25. 7 Is there a simple example?[ Laskaris & Ioannides, Clin. Neurophys., 2001 ]
  26. 26. 120 trials,Repeated stimulation binaural-stimulation [ 1kHz tones, 0.2s, 45 dB ], ISI: 3sec, passive listening Task : to ‘‘explain’’ the averaged M100-response
  27. 27. The M100-peak emerges fromthe stimulus-induced phase-resetting
  28. 28. Phase reorganizationof the ongoing brain waves
  29. 29. 8 Is therea more elaborate example? [ Laskaris et al., NeuroImage, 2003 ]
  30. 30. A study ofglobal firing patterns
  31. 31. Their relationwith localized sources
  32. 32. and ….initiating events
  33. 33. 240 trials, pattern reversal,4.5 deg , ISI: 0.7 sec,passive viewing Single-Trial data in unorganized format
  34. 34. Single-Trial data summarizedvia ordered prototypesreflecting the variabilityof regional response dynamics
  35. 35. ‘‘The ongoing activitybefore the stimulus-onset is functionally coupled with the subsequent regional response’’
  36. 36. Polymodal Parietal AreasBA5 & BA7are the major sourcesof the observed variability
  37. 37. Regional vs Localresponse dynamics : There is relationship between N70m-response variability and activity in early visual areas.
  38. 38. 9 What has been the lesson, so far, from the analysisof Event-Related Dynamics ?
  39. 39. The ‘‘dangerous’’ equation
  40. 40. Where one can learn more about Mining Informationfrom encephalographic recordings ? //www.hbd.brain.riken.jp symposium: MEG in psychiatry (AUD4, Monday, 9.00 )
  41. 41. Laskaris@zeus.csd.auth.gr

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