Giiacomo Veneri PHD Dissertation

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

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
2
Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test

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
3

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
4

Attention and Motor control
(Corbetta2001, Osborne2011)
5

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.
6

PSYCHOLOGICAL TEST
Eye Tracking, TMT, ET
Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test
7

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)
8

Visual (conjunction) Search Test
E Search (Wolfe, 1994) Sequencing (Reitan, 1958)
... and others (Veneri 2010, Veneri 2012)
9

SELECTIVE ATTENTION FEATURES
EXTRACT
Psycological Test, Mathematical Method
Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test
10

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
11
DN
DT

Sequencing (2/2)
• Look for the best path (Veneri, Rosini 2012)
12

MOTOR CONTROL FEATURES
EXTRACTION
Wavelet Entropy
Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test
13

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.
14

Frequency Analysis
Fourier analysis
• A signal is a «sum» of a sine
curve
ECG Example
15

Wavelet and Entropy
Wavelet Multiscal
decomposition Wavelet (Mallat, 1989)
16

Decomposed Eye Signal
Original signal
Noise?
Main componet
17

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
18

RESULTS
Healthy Subjects and Patients
Methods Results
Attention
FE
Motor
Control
FE
TMT
ET
Healthy
Subjects
Patients
SCA2,NDC
Psychological Test
19

Despiking
Healthy Subject Patient
20

Despiking
21

Healthy Subjects
Clusters ROC (20% error rate)
22

Patients
P-value Clusters
23

Entropy levels
All levels Last level
24

Variance
Signal Signal on fixations
25

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
26

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).
27

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).
28

CONCLUSIONS
Tools and Hypothesis
29

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.
30

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
31

Study the influence
• Does the motor control (cerebellum) influence
selective attention?
32

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.
33

THANKS
Feature-Based Information Processing of Selective Attention through
Entropy Analysis system
34

Giacomo Veneri
35

Model
Energy Saccade length
36

Giiacomo Veneri PHD Dissertation

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