functional data analysis for the assessment
play

functional data analysis for the assessment of motor learning in - PowerPoint PPT Presentation

Oct 09, 2022 β€’174 likes β€’673 views

Structured stochastic processes and functional data analysis for the assessment of motor learning in normal and pathological subjects (Postdoctoral research submitted to FAPESP) Noslen Hernndez Antonio Galves, Claudia Vargas II NeuroMat

  1. Structured stochastic processes and functional data analysis for the assessment of motor learning in normal and pathological subjects (Postdoctoral research submitted to FAPESP) Noslen HernΓ‘ndez Antonio Galves, Claudia Vargas II NeuroMat Workshop: New Frontiers in Neuromathematics November 2016

  2. Recent results [ A. Duarte, R. Fraiman, A. Galves, G. Ost and C.D. Vargas, arXiv:1602.00579 ] o Hypothesis: The brain retrieves statistical regularities from stimuli

  3. Recent results [ A. Duarte, R. Fraiman, A. Galves, G. Ost and C.D. Vargas, arXiv:1602.00579 ] o Hypothesis: The brain retrieves statistical regularities from stimuli o Definition of new kind stochastic processes

  4. Recent results [ A. Duarte, R. Fraiman, A. Galves, G. Ost and C.D. Vargas, arXiv:1602.00579 ] o Hypothesis: The brain retrieves statistical regularities from stimuli o Definition of new kind stochastic processes Stochastic process driven by context tree model

  5. Recent results [ A. Duarte, R. Fraiman, A. Galves, G. Ost and C.D. Vargas, arXiv:1602.00579 ] o Hypothesis: The brain retrieves statistical regularities from stimuli o Definition of new kind stochastic processes Stochastic process driven by context tree model o Allows a design, modeling and analysis of neurophysiological experiments with structured stimuli

  6. Retrieving a context tree form EEG data Random Source Images taken from [A. Duarte, R. Fraiman, A. Galves, G. Ost and C.D. Vargas, arXiv:1602.00579]

  7. Retrieving a context tree form EEG data Random Source Images taken from [A. Duarte, R. Fraiman, A. Galves, G. Ost and C.D. Vargas, arXiv:1602.00579]

  8. Retrieving a context tree form EEG data Random Source Images taken from [A. Duarte, R. Fraiman, A. Galves, G. Ost and C.D. Vargas, arXiv:1602.00579]

  9. Retrieving a context tree form EEG data Random Source π‘Œ 1 , 𝑍 1 , … , π‘Œ π‘œ , 𝑍 o π‘œ o New statistical model selection procedure for FD o The brain effectively identifies the context tree characterizing the source. Images taken from [A. Duarte, R. Fraiman, A. Galves, G. Ost and C.D. Vargas, arXiv:1602.00579]

  10. Can such finding be corroborated in behavioral responses, specifically in execution of movements? Images taken from https://www.lvhn.org/for_referring_physicians/better_medicine/rehabilitation/neuromuscular_rehabilitation_technology_helps_patients_i ncrease_motor_control; http://neuromat.numec.prp.usp.br/pt-br/newsletter-31; https://lehacker.com/brain-facts-revealing/

  11. Can such finding be corroborated in behavioral responses, specifically in execution of movements? Random Source Images taken from https://www.lvhn.org/for_referring_physicians/better_medicine/rehabilitation/neuromuscular_rehabilitation_technology_helps_patients_i ncrease_motor_control; http://neuromat.numec.prp.usp.br/pt-br/newsletter-31; https://lehacker.com/brain-facts-revealing/

  12. Can such finding be corroborated in behavioral responses, specifically in execution of movements? Execution of Random movement Source Images taken from https://www.lvhn.org/for_referring_physicians/better_medicine/rehabilitation/neuromuscular_rehabilitation_technology_helps_patients_i ncrease_motor_control; http://neuromat.numec.prp.usp.br/pt-br/newsletter-31; https://lehacker.com/brain-facts-revealing/

  13. Can such finding be corroborated in behavioral responses, specifically in execution of movements? Rehabilitation of patients Execution of Random movement Source Goalkeeper Game Images taken from https://www.lvhn.org/for_referring_physicians/better_medicine/rehabilitation/neuromuscular_rehabilitation_technology_helps_patients_i ncrease_motor_control; http://neuromat.numec.prp.usp.br/pt-br/newsletter-31; https://lehacker.com/brain-facts-revealing/

  14. Stochastic processes driven by structured Markov chain o Finite alphabet 𝐡 o Sequence of stimuli π‘Œ π‘œ ∈ 𝐡 generated according to specific regular statistical pattern

  15. Stochastic processes driven by structured Markov chain o Finite alphabet 𝐡 o Sequence of stimuli π‘Œ π‘œ ∈ 𝐡 generated according to specific regular statistical pattern o Structured Markov chain π‘Œ π‘œ π‘œβˆˆβ„€ :

  16. Stochastic processes driven by structured Markov chain o Finite alphabet 𝐡 o Sequence of stimuli π‘Œ π‘œ ∈ 𝐡 generated according to specific regular statistical pattern o Structured Markov chain π‘Œ π‘œ π‘œβˆˆβ„€ : οƒ˜ for some π‘š , οƒ˜ any 𝑛 β‰₯ π‘š , ( π‘š, 𝑛 ∈ β„€ ) π‘œβˆ’1 = 𝑦 π‘œβˆ’π‘› , … , 𝑦 π‘œβˆ’1 ∈ 𝐡 𝑛 , οƒ˜ and any finite string 𝑦 π‘œβˆ’π‘› οƒ˜ : mapping assigning to each past string a corresponding class in a partition of the space of relevant pasts.

  17. Stochastic processes driven by structured Markov chain o Sequence of response 𝑍 π‘œ ∈ 𝒡 to the stimuli

  18. Stochastic processes driven by structured Markov chain o Sequence of response 𝑍 π‘œ ∈ 𝒡 to the stimuli o Stochastic processes driven by structured Markov chain

  19. Stochastic processes driven by structured Markov chain o Sequence of response 𝑍 π‘œ ∈ 𝒡 to the stimuli o Stochastic processes driven by structured Markov chain π‘Œ π‘œ π‘œβˆˆβ„€ is a structured Markov chain. οƒ˜ οƒ˜ 𝑍 1 , 𝑍 2 , … are independent variables conditionally to the sequence π‘Œ π‘œ π‘œβˆˆβ„€ for any measurable 𝐾

  20. Stochastic processes driven by structured Markov chain o Sequence of response 𝑍 π‘œ ∈ 𝒡 to the stimuli o Stochastic processes driven by structured Markov chain π‘Œ π‘œ π‘œβˆˆβ„€ is a structured Markov chain. οƒ˜ οƒ˜ 𝑍 1 , 𝑍 2 , … are independent variables conditionally to the sequence π‘Œ π‘œ π‘œβˆˆβ„€ for any measurable 𝐾 Stochastic processes driven by context tree model is an outstanding example of SPDSMC Related works: J.Garcia et al., arXiv:1002.0729 (2010); V. JÀÀskinen et al., Scand. J. Stat (2014)

  21. Prob1: Design of suitable experiments to evaluate evidence about learning statistical regularities

  22. Prob1: Design of suitable experiments to evaluate evidence about learning statistical regularities We need to carefully specify: o An alphabet 𝐡 o Response variable 𝑍 π‘œ

  23. Prob1: Design of suitable experiments to evaluate evidence about learning statistical regularities We need to carefully specify: o An alphabet 𝐡 o Response variable 𝑍 π‘œ Goalkeeper Game 0: center; 1: right; 2: left

  24. Prob1: Design of suitable experiments to evaluate evidence about learning statistical regularities We need to carefully specify: o An alphabet 𝐡 o Response variable 𝑍 π‘œ Goalkeeper Game 0: center; 1: right; 2: left 𝐸 π‘œ Curves of spatial position, Videos gathered by sensors or camera

  25. Prob1: Design of suitable experiments to evaluate evidence about learning statistical regularities We need to carefully specify: o An alphabet 𝐡 o Response variable 𝑍 π‘œ Goalkeeper Game 0: center; 1: right; 2: left 𝐸 π‘œ Curves of spatial position, Videos gathered by sensors or camera 𝑍 π‘œ ∈ 𝐡 direction

  26. Prob1: Design of suitable experiments to evaluate evidence about learning statistical regularities We need to carefully specify: o An alphabet 𝐡 o Response variable 𝑍 π‘œ Goalkeeper Game 0: center; 1: right; 2: left 𝐸 π‘œ Curves of spatial position, Videos gathered by sensors or camera 𝑍 π‘œ ∈ 𝐡 𝑍 π‘œ ∈ 𝒡 FD representing trajectory direction

  27. Prob1: Design of suitable experiments to evaluate evidence about learning statistical regularities We need to carefully specify: o An alphabet 𝐡 o Response variable 𝑍 π‘œ Goalkeeper Game 0: center; 1: right; 2: left 𝐸 π‘œ Curves of spatial position, Videos gathered by sensors or camera 𝑍 π‘œ ∈ 𝐡 𝑍 π‘œ ∈ 𝒡 𝑍 π‘œ ∈ 𝑍 = 0,1,2 Γ— *𝐻, 𝐢+ FD representing trajectory direction

  28. o Kind of statistical pattern of the stimuli sequence π‘Œ π‘œ π‘œβˆˆπ‘Ž Context tree model compatible with 𝜐, π‘ž ; π‘ž = π‘ž β‹… π‘₯ ∢ π‘₯ ∈ 𝜐

  29. o Kind of statistical pattern of the stimuli sequence π‘Œ π‘œ π‘œβˆˆπ‘Ž Context tree model compatible with 𝜐, π‘ž ; π‘ž = π‘ž β‹… π‘₯ ∢ π‘₯ ∈ 𝜐 π‘₯ 2 π‘₯ 3 π‘₯ 1 π‘₯ 4 𝑄 𝜐

  30. o Kind of statistical pattern of the stimuli sequence π‘Œ π‘œ π‘œβˆˆπ‘Ž Context tree model compatible with 𝜐, π‘ž ; π‘ž = π‘ž β‹… π‘₯ ∢ π‘₯ ∈ 𝜐 …….. 𝑏 …… π‘₯ 2 βˆ’π‘œ = 𝑦 βˆ’1 βˆ’π‘œ = 𝑄(π‘Œ 0 = 𝑏|𝑑 𝜐 (𝑦 βˆ’1 βˆ’π‘œ )) 𝑄 π‘Œ 0 = 𝑏|π‘Œ βˆ’1 π‘₯ 3 π‘₯ 1 π‘₯ 4 𝑄 𝜐

  31. o Kind of statistical pattern of the stimuli sequence π‘Œ π‘œ π‘œβˆˆπ‘Ž Context tree model compatible with 𝜐, π‘ž ; π‘ž = π‘ž β‹… π‘₯ ∢ π‘₯ ∈ 𝜐 …….. 𝑏 …… π‘₯ 2 βˆ’π‘œ = 𝑦 βˆ’1 βˆ’π‘œ = 𝑄(π‘Œ 0 = 𝑏|𝑑 𝜐 (𝑦 βˆ’1 βˆ’π‘œ )) 𝑄 π‘Œ 0 = 𝑏|π‘Œ βˆ’1 π‘₯ 3 π‘₯ 1 π‘₯ 4 𝑄 𝜐

  32. o Kind of statistical pattern of the stimuli sequence π‘Œ π‘œ π‘œβˆˆπ‘Ž Context tree model compatible with 𝜐, π‘ž ; π‘ž = π‘ž β‹… π‘₯ ∢ π‘₯ ∈ 𝜐 …….. 𝑏 …… π‘₯ 2 π‘₯ 3 π‘₯ 1 𝑄 π‘₯ 4 𝑑

  33. o Kind of statistical pattern of the stimuli sequence π‘Œ π‘œ π‘œβˆˆπ‘Ž Context tree model compatible with 𝜐, π‘ž ; π‘ž = π‘ž β‹… π‘₯ ∢ π‘₯ ∈ 𝜐 …….. 𝑏 …… π‘₯ 2 π‘₯ 3 π‘₯ 1 𝑄 π‘₯ 4 𝑑 The subject might adopt a form of representation of the given Markov chain that involves a certain partition into classes that does not involves a context tree

Recommend

Analysis of Assessment Procedures Presentation and Analysis of Assessment Data

Analysis of Assessment Procedures Presentation and Analysis of Assessment Data

Entry 3, Part 3 Analysis of Assessment Procedures Presentation and Analysis of Assessment Data Pre-Assessment/Diagnostic Assessment Data Analyze your pre-assessment results and discuss these results in reference to your learning goals and

49 views β€’ 3 slides
AB Introduction Functional data occurs for example in time series analysis, chemometry and

AB Introduction Functional data occurs for example in time series analysis, chemometry and

Introduction to Functional Data Analysis Elia Liiti ainen ( eliitiai@cc.hut.fi ) Time Series Prediction Group Adaptive Informatics Research Centre Helsinki University of Technology, Finland January 30, 2007 AB Introduction Functional data

508 views β€’ 22 slides
Fundamental Issues in Bayesian Functional Data Analysis Dennis D. Cox Rice University 1

Fundamental Issues in Bayesian Functional Data Analysis Dennis D. Cox Rice University 1

Fundamental Issues in Bayesian Functional Data Analysis Dennis D. Cox Rice University 1 Introduction Question: What are functional data? Answer: Data that are functions of a continuous variable. ... say we observe Y i ( t ), t [

648 views β€’ 45 slides
Functional data analysis with the refund package Philip T. Reiss University of Haifa

Functional data analysis with the refund package Philip T. Reiss University of Haifa

Functional data analysis Splines refund fMRI example References Functional data analysis with the refund package Philip T. Reiss University of Haifa reiss@stat.haifa.ac.il http://works.bepress.com/phil_reiss Psychoco: International

503 views β€’ 26 slides
RECENT ADVANCEMENTS IN FUNCTIONAL ASSESSMENT M E T H O D S A N D A P P L I C AT I O N S NCABA

RECENT ADVANCEMENTS IN FUNCTIONAL ASSESSMENT M E T H O D S A N D A P P L I C AT I O N S NCABA

RECENT ADVANCEMENTS IN FUNCTIONAL ASSESSMENT M E T H O D S A N D A P P L I C AT I O N S NCABA 2017 C A R O L E VA N C A M P, P H . D . Functional Assessment A process by which the variables influencing problem behavior are

1.15k views β€’ 66 slides
Functional Linear Models 1 66 / 181 Functional Linear Models Statistical Models So far we have

Functional Linear Models 1 66 / 181 Functional Linear Models Statistical Models So far we have

Functional Linear Models Functional Linear Models 1 66 / 181 Functional Linear Models Statistical Models So far we have focussed on exploratory data analysis Smoothing Functional covariance Functional PCA Now we wish to examine predictive

1.04k views β€’ 43 slides
tidyfun : Tidy Functional Data A new framework for working with functional data in R Fabian Scheipl

tidyfun : Tidy Functional Data A new framework for working with functional data in R Fabian Scheipl

tidyfun : Tidy Functional Data A new framework for working with functional data in R Fabian Scheipl 1 Jeff Goldsmith 2 1 : Dept. of Statistics, LMU Munich 2 : Columbia University Mailman School of Public Health Functional Data Painful to work with:

969 views β€’ 61 slides
FUNCTIONAL ASSESSMENT FOR EARLY INTERVENTION Module 4 1 INTRODUCTIONS Who am I? Who

FUNCTIONAL ASSESSMENT FOR EARLY INTERVENTION Module 4 1 INTRODUCTIONS Who am I? Who

FUNCTIONAL ASSESSMENT FOR EARLY INTERVENTION Module 4 1 INTRODUCTIONS Who am I? Who are you? Expectations Syllabus Change- Module 4 quiz due 2/10/13 2 CLASS FORMAT Best Practices in Instruction Data Based Decision

541 views β€’ 30 slides
Functional Data Analysis using Topological Summary Statistics NSF TRIPODS Workshop: Geometry and

Functional Data Analysis using Topological Summary Statistics NSF TRIPODS Workshop: Geometry and

Functional Data Analysis using Topological Summary Statistics NSF TRIPODS Workshop: Geometry and Topology of Data Lorin Crawford Department of Biostatistics Center for Statistical Sciences Center for Computational Molecular Biology Brown

967 views β€’ 63 slides
Conceptual Assessment of Energy Input Output Analysis and Data Envelopment Analysis of

Conceptual Assessment of Energy Input Output Analysis and Data Envelopment Analysis of

Conceptual Assessment of Energy Input Output Analysis and Data Envelopment Analysis of Greenhouse Crops on Crete Island, Greece Mohamed Elhag 1 and Silvena Boteva 2 1 Department of Hydrology and Water Resources Management, Faculty of

338 views β€’ 16 slides
Quantifying prosodic boundary strength using functional data analysis of articulatory movement

Quantifying prosodic boundary strength using functional data analysis of articulatory movement

Quantifying prosodic boundary strength using functional data analysis of articulatory movement 2nd Pan-American/Iberian Meeting on Acoustics November 2010, Cancun, Mexico Benjamin Parrell, Sungbok Lee, & Dani Byrd Department of

418 views β€’ 24 slides
Clinical Inference in the Assessment of Mental Residual Functional Capacity David J Schretlen,

Clinical Inference in the Assessment of Mental Residual Functional Capacity David J Schretlen,

Clinical Inference in the Assessment of Mental Residual Functional Capacity David J Schretlen, PhD, ABPP OIDAP Panel Meeting 10 June 2009 1 Methods of Inference 1. Pathognomonic sign approach 2. Pattern analysis 3. Level of performance or

761 views β€’ 44 slides
Topological Data Analysis for Brain Networks Relating Functional Brain Network Topology to

Topological Data Analysis for Brain Networks Relating Functional Brain Network Topology to

Topological Data Analysis for Brain Networks Relating Functional Brain Network Topology to Clinical Measures of Behavior Bei Wang Phillips 1 , 2 University of Utah Joint work with Eleanor Wong 1 , 2 , Sourabh Palande 1 , 2 , Brandon Zielinski 3 ,

430 views β€’ 30 slides
ActiveNetworks Cross-Condition Analysis of Functional Genomic Data T. M. Murali , Deept Kumar

ActiveNetworks Cross-Condition Analysis of Functional Genomic Data T. M. Murali , Deept Kumar

ActiveNetworks Cross-Condition Analysis of Functional Genomic Data T. M. Murali , Deept Kumar , Greg Grothaus , Maulik Shukla , Graham Jack , Jamie Garst , Richard Helm , Malcolm Potts , and Naren Ramakrishnan

762 views β€’ 47 slides
Dynamic and Steady-State Process Investigations Using Functional Data Analysis P. James McLellan

Dynamic and Steady-State Process Investigations Using Functional Data Analysis P. James McLellan

Dynamic and Steady-State Process Investigations Using Functional Data Analysis P. James McLellan Department of Chemical Engineering Queens University Kingston, ON Canada mclellnj@chee.queensu.ca 1 Outline Brief Exercise

801 views β€’ 60 slides
Assessment of Fuzzy Gait Assessment . . . Explanation of the . . . Functional Impairment

Assessment of Fuzzy Gait Assessment . . . Explanation of the . . . Functional Impairment

Formulation of the . . . Need for Fuzzy . . . Pre-Processing of Gait . . . Assessment of Fuzzy Gait Assessment . . . Explanation of the . . . Functional Impairment Conclusion and Future . . . Acknowledgment in Human Locomotion: Proof of

422 views β€’ 16 slides
SHAPE ANALYSIS OF FUNCTIONAL DATA Anuj Srivastava Joint work with Sutanoy Dasgupta, Ian Jermyn,

SHAPE ANALYSIS OF FUNCTIONAL DATA Anuj Srivastava Joint work with Sutanoy Dasgupta, Ian Jermyn,

SHAPE ANALYSIS OF FUNCTIONAL DATA Anuj Srivastava Joint work with Sutanoy Dasgupta, Ian Jermyn, Debdeep Pati Department of Statistics, Florida State University Presented at Statistical Modeling for Shapes and Imaging Workshop, IHP , Paris,

1.36k views β€’ 41 slides
STATS 507 Data Analysis in Python Lecture 6: Functional Programming with itertools and functools

STATS 507 Data Analysis in Python Lecture 6: Functional Programming with itertools and functools

STATS 507 Data Analysis in Python Lecture 6: Functional Programming with itertools and functools Functional Programming In the last lecture, we saw ideas from object oriented programming Everything is an object Every operation is the

1.1k views β€’ 68 slides
Part I: Functional Programming A pure functional program consists of data, functions, and an

Part I: Functional Programming A pure functional program consists of data, functions, and an

Part I: Functional Programming A pure functional program consists of data, functions, and an expression which describes a result. Missing: variables, assignment, side-effects. A processor of a functional program is essentially a

833 views β€’ 36 slides
Functional Data Structures [C. Okasaki, Simple and efficient purely functional queues and deques ,

Functional Data Structures [C. Okasaki, Simple and efficient purely functional queues and deques ,

Functional Data Structures [C. Okasaki, Simple and efficient purely functional queues and deques , J. of Functional Programming, 5(4), 583-592, 1995] [H. Kaplan, R. Tarjan, Purely functional, real-time deques with catenation , Journal of the ACM,

368 views β€’ 8 slides
Evaluation of a novel device for functional respiratory assessment in horses Lucy Sheard

Evaluation of a novel device for functional respiratory assessment in horses Lucy Sheard

Evaluation of a novel device for functional respiratory assessment in horses Lucy Sheard University of Bristol Supervised by Dr. J Burn and Dr. K Allen Introduction: Background Respiratory assessment in poor performing racehorses Failure to

509 views β€’ 12 slides
NATIONAL ASSESSMENT PROGRAM: LITERACY AND NUMERACY (NAPLAN) DATA ANALYSIS 2018 CHURCHLANDS

NATIONAL ASSESSMENT PROGRAM: LITERACY AND NUMERACY (NAPLAN) DATA ANALYSIS 2018 CHURCHLANDS

NATIONAL ASSESSMENT PROGRAM: LITERACY AND NUMERACY (NAPLAN) DATA ANALYSIS 2018 CHURCHLANDS PRIMARY SCHOOL SCHOOL BOARD PRESENTATION Numeracy Data Analysis Cohort performed above Like schools in Numeracy. Year 3 has progressed

430 views β€’ 19 slides
EIA Data Collection &Analysis During Energy Emergencies State Energy Risk Assessment Workshop

EIA Data Collection &Analysis During Energy Emergencies State Energy Risk Assessment Workshop

EIA Data Collection &Analysis During Energy Emergencies State Energy Risk Assessment Workshop Susan Grissom April 28, 2015 | Denver, Colorado U.S. Energy Information Administration Independent Statistics & Analysis www.eia.gov

434 views β€’ 18 slides
Functional regression analysis using R Christian Ritz Statistics Group Faculty of Life Sciences

Functional regression analysis using R Christian Ritz Statistics Group Faculty of Life Sciences

Functional regression analysis using R Christian Ritz Statistics Group Faculty of Life Sciences (LIFE) University of Copenhagen, Denmark Dortmund, August 13 2008 Christian Ritz (Statistics at LIFE) 1 / 15 Examples What are functional data?

907 views β€’ 19 slides

More recommend