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

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

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
Catching Events in Video Streams Mohan M. Trivedi Computer Vision and Robotics Research

Catching Events in Video Streams Mohan M. Trivedi Computer Vision and Robotics Research

Catching Events in Video Streams Mohan M. Trivedi Computer Vision and Robotics Research Laboratory Electrical and Computer Engineering Department Jacobs School of Engineering Research Review February 28, 2003 Event Catching in Video Streams

384 views β€’ 21 slides
ARTIFICIAL INTELLIGENCE Summary Lecturer: Silja Renooij These slides are part of the INFOB2KI

ARTIFICIAL INTELLIGENCE Summary Lecturer: Silja Renooij These slides are part of the INFOB2KI

Utrecht University INFOB2KI 2019-2020 The Netherlands ARTIFICIAL INTELLIGENCE Summary Lecturer: Silja Renooij These slides are part of the INFOB2KI Course Notes available from www.cs.uu.nl/docs/vakken/b2ki/schema.html Subject overview

572 views β€’ 24 slides
C HAPTER 10 CORE MECHANICS ZHONGSHI XI RACHEL MAILACH CORE MECHANICS: -determine how the game

C HAPTER 10 CORE MECHANICS ZHONGSHI XI RACHEL MAILACH CORE MECHANICS: -determine how the game

C HAPTER 10 CORE MECHANICS ZHONGSHI XI RACHEL MAILACH CORE MECHANICS: -determine how the game actually operates -heart of the game -defines rules and game-play Quiz 1 What are the core mechanics of Tetris? A. A row is eliminated if and

707 views β€’ 42 slides
Information Architecture Session 33 INST 346 Technologies, Infrastructure and Architecture

Information Architecture Session 33 INST 346 Technologies, Infrastructure and Architecture

Information Architecture Session 33 INST 346 Technologies, Infrastructure and Architecture Content Management Systems Database to store content Also stores access control data and parameters PHP to control user experience

538 views β€’ 30 slides
Nordic and Baltic Regions in a European Development and Policy Context Norwegian Institute for

Nordic and Baltic Regions in a European Development and Policy Context Norwegian Institute for

NORDIC NORBA Nordic and Baltic Regions in a European Development and Policy Context Norwegian Institute for Urban and Regional Research Oslo, Norway, 14 th -15 th March 2012 Steinar Johansen and Marte Bjrnson NIBR, Neil Adams and Phil Pinch

835 views β€’ 34 slides
THE GOSPEL OF MARK Outline What Is a Gospel? The Lives of Mark Academic

THE GOSPEL OF MARK Outline What Is a Gospel? The Lives of Mark Academic

Class 2 THE GOSPEL OF MARK Outline What Is a Gospel? The Lives of Mark Academic Approaches to (Sacred) Texts Literary Criticism, & its Subset, Narrative Criticism Literary criticism in general Narrative criticism

567 views β€’ 35 slides
Research, Generating Ideas, and Reading Literature CSE545 - Fall 2006 Introduction Computer and

Research, Generating Ideas, and Reading Literature CSE545 - Fall 2006 Introduction Computer and

362 views β€’ 18 slides
Navigating Balance Deepha Romuwalt RPT , C/NDT Proactive Physio Plus 1796 16 th Street East

Navigating Balance Deepha Romuwalt RPT , C/NDT Proactive Physio Plus 1796 16 th Street East

Navigating Balance Deepha Romuwalt RPT , C/NDT Proactive Physio Plus 1796 16 th Street East Owen Sound N4K 5N3 1 proactivephysioplus@gmail.com www.proactivephysioplus.ca Balance u It is the product of integrated inputs to the body as a

451 views β€’ 18 slides

More recommend