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Giacomo Veneri 2012 phd dissertation

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Study the influence of (eye) motor control on selective attention
Develop a method to extract motor control parameters during visual search
Develop a method to extract selective attention features during visual search

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Giacomo Veneri 2012 phd dissertation

  1. 1. Feature-Based Information Processing of Selective Attention through Entropy Analysis system Giacomo Veneri November 2012 Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 1
  2. 2. Objectives • Study the influence of (eye) motor control on selective attention • Develop a method to extract motor control parameters during visual search • Develop a method to extract selective attention features during visual search Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 2 Methods Results Attention FE Motor Control FE TMT ET Healthy Subjects Patients SCA2,NDC Psychological Test
  3. 3. Selective Attention • Selective attention ( Posner, 1980) is the process to select some region of the scene to be processed in detail; then, selective attention works as filter. • Top-Down: attentional process that influences sensory processing in an automatic and persistent manner • Bottom-Up: influence on the nervous system due to extrinsic properties of the stimuli Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 3
  4. 4. Motor Control and Cerebellum • The neuronal circuitry of the cerebellum is thought to encode internal models that reproduce the dynamic properties of body parts (Kelly2003,Ito2005,Ito2006a). • These models control the movement allowing the brain to precisely control the movement without the need for sensory feedback (Barlow2002,Ito2008,King2011 ) • SCA2 and NDC Patients Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 4
  5. 5. Attention and Motor control (Corbetta2001, Osborne2011) Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 5
  6. 6. Methods 1. Veneri, G., Federighi, P., Rosini, F., Federico, A., & Rufa, A. (2010). Influences of data filtering on human-computer interaction by gaze-contingent display and eye-tracking applications. Computers in Human Behavior , 26 (6), 1555 - 1563. doi: 10.1016/j.chb.2010.05.030 [SCOPUS, ACM] 2. Veneri, G., Federighi, P., Rosini, F., Federico, A., & Rufa, A. (2011, Mar). Spike removal through multiscale wavelet and entropy analysis of ocular motor noise: A case study in patients with cerebellar disease. Journal of Neuroscience Methods , 196 (2), 318–326. doi: 10.1016/j.jneumeth.2011.01.006 [MEDLINE, SCOPUS] 3. Veneri, G., Piu, P., Rosini, F., Federighi, P., Federico, A., & Rufa, A. (2011). Automatic eye fixations identification based on analysis of variance and covariance. Pattern Recognition Letters , 32 (13), 1588 - 1593. doi: 10.1016/j.patrec.2011.06.012 [SCOPUS] 4. Veneri, G., Pretegiani, E., Rosini, F., Federighi, P., Federico, A., & Rufa, A. (2011, Mar). Evaluating the human ongoing visual search performance by eye tracking application and se-quencing tests. Comput Methods Programs Biomed . Retrieved from http://dx.doi.org/10.1016/j.cmpb.2011.02.006 doi:10.1016/j.cmpb.2011.02.006 [SCOPUS. MEDLINE, ACM] 5. Veneri, G., Rosini, F., Federighi, P., Federico, A., & Rufa, A.(2012, Feb). Evaluating gaze control on a multi-target sequenc-ing task: The distribution of fixations is evidence of exploration optimisation. Comput Biol Med , 42 (2), 235–244. Retrieved from http://dx.doi.org/10.1016/j.compbiomed.2011.11.013 doi: 10.1016/j.compbiomed.2011.11.013 [SCOPUS. MEDLINE, ACM] InProceedings 1. Veneri, G., Federighi, P., Pretegiani, E., Rosini, F., Federico, A., & Rufa, A. (2009). Eye tracking - stimulus integrated semi automatic case base system. In Proceeding of the 13th world multi-conference on systemics, cybernetics and informatics. 2. Veneri, G., Pretegiani, E., Federighi, P., Rosini, F., & Rufa, A. (2010). Evaluating human visual search performance by monte carlo methods and heuristic model. In IEEE (Ed.), 10th ieee international conference on information technology and applications in biomedicine (itab 2010). [SCOPUS, IEEE] 3. Veneri, G., Piu, P., Federighi, P., Rosini, F., Federico, A., & Rufa, A. (2010, jun.). Eye fixations identification based on statistical analysis - case study. In Cognitive information processing (cip), 2010 2nd international workshop on (p. 446 -451). IEEE. doi: 10.1109/CIP.2010.5604221 [SCOPUS, IEEE] Others (posters) 1. Veneri, G., Federighi, P., Rosini, F., Pretegiani, E., Federico, A., & Rufa, A. (2009). The role of latest fixations on ongoing visual search: a model to evaluate the selection mechanism. In Rovereto workshop of attention. 2. Veneri, G., Olivetti, E., Avesani, P., Federico, A., & Rufa, A. (2011). Bayesian hypothesis on selective attention. In Rovereto visual attention congress. Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 6
  7. 7. PSYCHOLOGICAL TEST Eye Tracking, TMT, ET Methods Results Attention FE Motor Control FE TMT ET Healthy Subjects Patients SCA2,NDC Psychological Test Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 7
  8. 8. Eye Tracking • Eye tracking is the process of measuring either the point of gaze (where one is looking) or the motion of an eye relative to the head. • ASL 3000 (240Hz) Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 8
  9. 9. Visual (conjunction) Search Test E Search (Wolfe, 1994) Sequencing (Reitan, 1958) ... and others (Veneri 2010, Veneri 2012)Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 9
  10. 10. SELECTIVE ATTENTION FEATURES EXTRACT Psycological Test, Mathematical Method Methods Results Attention FE Motor Control FE TMT ET Healthy Subjects Patients SCA2,NDC Psychological Test Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 10
  11. 11. Attention Features Extraction 1/2 Common Method • Visited ROI • Reaction Time Our geometric Method (Veneri, Rosini 2012) • Distance to nearest Target • Distance to Nearest ROI • Sequencing Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 11 DN DT
  12. 12. Sequencing (2/2) • Look for the best path (Veneri, Rosini 2012) Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 12
  13. 13. MOTOR CONTROL FEATURES EXTRACTION Wavelet Entropy Methods Results Attention FE Motor Control FE TMT ET Healthy Subjects Patients SCA2,NDC Psychological Test Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 13
  14. 14. Motor Control Noise Evaluation • (Beers2007, Veneri2011) gaze noise may be additive with or multiplicative of the eye movement, and is lost in recording noise (RN) due to blinks or signal loss; • noise = PN + RN = SDN (signal) + ADN + RN where SDN is physiological signal dependent noise and ADN physiological additive noise. Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 14
  15. 15. Frequency Analysis Fourier analysis • A signal is a «sum» of a sine curve ECG Example Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 15
  16. 16. Wavelet and Entropy Wavelet Multiscal decomposition Wavelet (Mallat, 1989) Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 16
  17. 17. Decomposed Eye Signal Original signal Noise? Main componet Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 17
  18. 18. Wavelet Entropy The idea (Veneri 2011) • After decomposition • We removed spikes • We evaluated Entropy • Entropy is the measure of the chaos on a system Algorithm Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 18
  19. 19. RESULTS Healthy Subjects and Patients Methods Results Attention FE Motor Control FE TMT ET Healthy Subjects Patients SCA2,NDC Psychological Test Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 19
  20. 20. Despiking Healthy Subject Patient Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 20
  21. 21. Despiking Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 21
  22. 22. Healthy Subjects Clusters ROC (20% error rate) Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 22
  23. 23. Patients P-value Clusters Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 23
  24. 24. Entropy levels All levels Last level Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 24
  25. 25. Variance Signal Signal on fixations Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 25
  26. 26. Before conclusions • Proposed Wavelet Entropy Implementation is NOT noise on fixations or noise of global signal • Proposed Wavelet Entropy Implementation «catches» motor noise topical featurese of each subject (colored noise) • Wavelet Type or levels are critical Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 26
  27. 27. Selective attention • DT provided a indicator to under- stand the ability of humans to converge to the target. • ANOVA reported significant difference among groups (F (2, 35) = 9.476, p < 0.01) • post-hoc Sidak procedure confirmed significant difference between – CTRL-SCA2 (p CTRL−SCA2 < 0.01), – CTRL-NDC (p NDC−SCA2 ≤ 0.01); – no significant dif-ference was found between SCA2-NDC (p SCA2−NDC = 0.622). Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 27
  28. 28. Correlation DT-E • Pearson and Spearman test reported correlation between E and DT for NDC patients (p < 0.05, ρ = 0.892, A), and correlation for SCA2 patients (p < 0.05, ρ = 0.736, B) not confirmed by Spearman (p = 0.18). No correlation was found for CTRL subjects (p = 0.43). Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 28
  29. 29. CONCLUSIONS Tools and Hypothesis Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 29
  30. 30. Summary • In the current work two methods have been developed: • Selective attention evaluation • Entropy analysis through wavelet decomposition. • Both methods are based on eye tracking • Subjects and patients cannot control eye movements or fixations perfectly, then, analysing eye motor entropy it is possible to extract some important features and conclusions. Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 30
  31. 31. Tool 1. Import Eye gaze data 2. Export Eye gaze data 3. Fixations recognition (Veneri, Piu, et al., 2010, 2011; Salvucci & Gold- berg, 2000) 4. Saccades recognition (Fischer et al., 1993) 5. TMT sequencing analysis 6. Transition Matrix analysis 7. ROI Analysis 8. Experiment segmentation Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 31
  32. 32. Study the influence • Does the motor control (cerebellum) influence selective attention? Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 32
  33. 33. Cerebellum could influence selective attention (Top-Down) sending afferent information of noise in order to minimize the functional cost of energy. Our hypothesis is systematically supported by recent application of opti-mal control theory; (Najemnik & Geisler, 2005), (Beers, 2007) and (Osborne, 2011) argued that humans’ vision is an optimal mechanism minimizing the effect of motor or cognitive noise. Our findings are compatible with this hypothesis: patients preferred sparser fixations avoiding saccade directed to the target. The non correlation of DN with WS suggested that this mechanism was a strategy to minimize the effort to control saccade rather than a direct influence on visual search. Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 33
  34. 34. THANKS Feature-Based Information Processing of Selective Attention through Entropy Analysis system Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 34
  35. 35. Model Energy Saccade length Giacomo Veneri – EVALab - Dep. Neurological and Behavioral Science - UNISI 35

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