Neurobotix Final PowerPoint


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Neurobotix Final PowerPoint

  1. 1. NeurobotixBishestha AdhikariArthur AssamoiMorgan AuzenneNicholas Helmstetter
  2. 2. Summary
  3. 3. Problem• Education is the main foundation of life• 1 of every 88 children is diagnosed withautism (NIMH)• The current state of autism education isseparated from its optimum potential bythe lack of incorporation of currenttechnology
  4. 4. Current Solutions• Children are taught using several educational devicesand programs such as Hatch TeachSmartTMand SmartInteractive Whiteboards• Inability to accurately assess the attention of a childwhile using educational material• Need to aid teachers in evaluating educationalprogress of children• Current methods of progress monitoring are notoptimized for children with cognitive disabilities
  5. 5. External Collaborator• Hatch is a leadingprovider of earlychildhood educationaltechnology• Provides adaptiveteaching technology in alllevels of school toenhance the educationsystem
  6. 6. External Collaborator• Hatch has provided:– Smart Interactive Whiteboard– JORO Pro Lift Stand– TeachSmartTMsoftware– Lenovo computer• We will provide to Hatch:– Preliminary data to determineapplicability of biometric tools toassist technology basededucational activities– Device testing, building andimplementation, design, buildingand testing of a prototype
  7. 7. Specific Aims• Detect the attention level of a child• Assist instructors in determining the child’slevel of interest during use of educationaltechnology
  8. 8. Device SpecificationsProduct Specification Design SpecificationDetects EEG Signals1) EEG headset used to determine attention towards educationalmaterial2) Output chart of attention and meditation scores for duration ofuse3) Visible real-time indications of attention levelsAids Teacher Assessment of Children1) In program feature to allow teachers to attach notes to specificattention sample2) Output notes as .txt file for later useEase of Use 1) Adjustable or one-size-fits-all design2) Less than one minute for setupUnobtrusive to Child During Operation1) EEG headset is integrated into a child friendly design to reducedistraction or anxietyReliability 1) Requires little to no maintenance between testsSafety 1) Device must conform to electronics safety standards
  9. 9. Gantt ChartResearch andInitial TestingAll GroupMembersAll GroupMembersEmotiv Epoc EEGHeadsetPreliminaryTestingNicholasHelmstetter &BishesthaAdhikariNicholasHelmstetter &BishesthaAdhikariNicholasHelmstetter &BishesthaAdhikariArthur Assamoi& BishesthaAdhikariArthur Assamoi& BishesthaAdhikariHall EffectResearch andPreliminaryTestingMorganAuzenne &Arthur AssamoiMorgan Auzenne& Arthur AssamoiMorgan Auzenne& ArthurAssamoiEye/GazeTracking ResearchBishesthaAdhikari &Morgan AuzenneBishesthaAdhikari &Morgan AuzenneBishesthaAdhikari &MorganAuzenneLaser TrackingConstruction andPreliminaryTestingNicholasHelmstetterNicholasHelmstetterCursor TrackingResearchNicholasHelmstetterNicholasHelmstetterNeurosSky EEGand LabVIEWConstructionNicholasHelmstetterNicholasHelmstetterNeuroSky EEGHeadset andLabVIEW ProgramTestingAll GroupMembersData AnalysisNicholasHelmstetterFinal PresentationAll GroupMembersSeptember October November December January February March April May
  10. 10. Design Constraints• Short setup time required when working with children(ECEC Ruston, LA)• Child friendly headset required to reduce anxiety (HolyAngels, Shreveport, LA)• Near real time visual data reporting (Caddo ParishSchool System, LA)• Easy-to-use interface enabling quick retrieval andanalysis of useful information (Gilmore Foundation, MS)
  11. 11. Design Alternatives• Multiple EEG Headsets Considered:– Emotiv Epoc EEG Headset– NeuroSky MindWave Headset• Multiple Positional Tracking Technologies Considered:– Eye Tracking Glasses– Infrared Laser Tracking– Gyroscope– Proximity Sensors– Hall Effect Sensors– Cursor Tracking Programs
  12. 12. Design AlternativesEmotiv Epoc HeadsetNeuroSky MindWave
  13. 13. Design DecisionCriteria Weight (%)Design AlternativeEmotiv Epoc NeuroSky MindWaveRating WeightedRatingRating WeightedratingRaw EEG 5 4 0.2 2 0.1Data Collection 15 1 0.15 3 0.45Setup Time 30 1 0.3 4 1.2User Comfort 10 1 0.1 4 0.4Cost 10 2 0.2 4 0.4Reliability 30 4 1.2 3 0.9Total 100 2.15 3.45Rating ValueUnsatisfactory 1Tolerable 2Good 3Very Good 4
  14. 14. NeuroSky Headset• Commercially availableheadset• Low Cost– Available versions rangefrom $70.00 - $200.00• Biosensor measuresbrainwave impulses fromFP1 and EMG from ear lobe• Capable of measuringAlpha, Beta, Gamma, Delta,and Theta brain waves
  15. 15. NeuroSky Headset• Utilizes ThinkGearTMchipto communicate withLabVIEW programmingvia Bluetooth• Capable of outputtingraw EEG signals,attention and meditationscores, and blinkstrength detection
  16. 16. Determining Attention• Neural bio-recorder used as inputwhich measures and interprets brainactivities• The application of a single electrodemeasures the change in fieldpotential over time arising fromsynaptic current and forms the basisfor EEG• Readings are inferred fromprocessing beta and alpha waveformactivity• Provides two 100-scale outputsoperating at 0.5 Hz described by theThinkGearTMchip as “Attention” and“Meditation”Robelledo-Mendez, 2009
  17. 17. Determining Attention• Alpha Rhythm– 8~13 Hz– Indication of physical relaxation and relativemental inactivity• Beta Rhythm– 13~35 Hz– Indication of mental activityNiedermeyer, 1999
  18. 18. Quantifying Attention• The active and reference electrodes in the EEG headset measure electrical potential• Electrical potential is supplied directly to the embedded chipset for filtering and separation• The relative power of the alpha and beta waves in relation to the total EEG signal can be used todetermine the cognitive state of person• The equations used for analysis are as follows:• N is the number of electrodes (one in this case), Pαk is the power in the alpha band for signal kand αi is the total power in the alpha band for all N signals at time window i• These variables are similar for the beta band• The power of the beta wave is multiplied by five because beta waves are usually smaller thanalpha waves by a factor of five• If αi > βi, then the state is relaxed• Otherwise, the state is attentiveGomez, 2002
  19. 19. Prototype
  20. 20. LabVIEW Block DiagramElapsed Time IndicatorFile CreationandNeuroSkyInitializationAttention and Meditation Score Retrieval and PlottingNote Taking
  21. 21. LabVIEW Front PanelNote TakingVisible CuesScores Plot
  22. 22. Testing Strategy• Purpose was to correlate attention scores tovarious activities• Verify NeuroSky/ThinkGearTMalgorithms forquantifying attention can be reproduciblycorrelated to mental states• Utilized several activities requiring varyingdegrees of mental activity• Recorded attention and meditation scores at 0.5Hz
  23. 23. Pearson’s CorrelationCoefficient AnalysisAttention vs. MeditationSubject # 1 – Viewing imageSample Attention Meditation10 84 6011 77 3812 74 44 Pearsons r13 80 56 0.20114 50 5115 78 7016 100 5117 96 3018 90 3519 50 2120 43 3721 60 5722 67 5023 61 3524 53 3525 47 1626 53 1427 80 4128 70 50Subject # 2 – Viewing ImageSample Attention Meditation17 74 6118 61 5319 75 56 Pearsons r20 61 57 0.16921 50 4322 61 3023 54 3724 60 6025 77 4126 57 2427 61 3828 64 3429 74 5730 94 6331 77 4832 66 7033 64 6134 26 11Subject # 3 – Viewing ImageSample Attention Meditation6 100 577 88 708 64 63 Pearsons r9 61 34 0.28810 88 4811 96 6612 80 4313 57 1714 67 3815 87 6716 69 8317 60 6918 78 5419 90 6020 74 5621 64 3022 66 2423 80 38
  24. 24. Pearson’s CorrelationCoefficient AnalysisSelf-reported Attention Score vs. Acquired Attention ScoreSubject # 1Activity Comparison Rank Average Attention ScoreGame 1 4 40.230Article 1 3 40.230Article 2 1 55.426Game 2 2 41.590Image 0 77.311Pearsons r = -0.879Subject # 2Activity Comparison Rank Average Attention ScoreGame 1 4 54.164Article 1 3 47.098Article 2 1 50.049Game 2 2 45.459Image 0 63.902Pearsons r = -0.482Subject # 3Activity Comparison Rank Average Attention ScoreGame 1 3 68.339Article 1 2 54.935Article2 1 32.090Game 2 4 44.629Image 0 67.426Pearsons r = -0.096
  25. 25. Histogram AnalysisPercent of Scores Over 50 – 98.36% Percent of Scores Over 50 – 67.21%Percent of Scores Over 50 – 86.89% Percent of Scores Over 50 – 57.38%
  26. 26. Histogram AnalysisPercent of Scores Over 50 – 86.76% Percent of Scores Over 50 – 22.39%
  27. 27. Conclusions• Attention and meditation scores not highlycorrelated• Self-reported scores not highly correlatedto acquired scores– Based on Pearson’s r correlation coefficient• Visible trend of attention for activities onindividual basis– Based on histogram trend analysis
  28. 28. Recap• The device meets most of the designspecifications• Allows teachers to attach notes to specificsamples• Setup time takes less than one minute• Child friendly design reduces distraction oranxiety• Little maintenance required between tests• Device meets all safety standards
  29. 29. Future Work• The specification that will need to be improved isDetecting EEG Signals• The LabVIEW program is able to detect and display theRaw EEG signals and the ThinkGearTMscores forattention and meditation• Obtaining individual EEG band data, particularly foralpha and beta waves, is the next step in dataacquisition• A more advanced EEG headset and/or additionalprogramming may be required• Create data analysis procedure for determining attentionfrom alpha and beta waveforms
  30. 30. Future Work• The next phase of testing is planned to include children– IRB approval will be necessary for this phase of testing– Incorporate more Hatch educational material into testingprocedure• Utilize Hatch educational software as primary interactivematerial for testing• Incorporate alternative methods of interaction between theSmart board system and the child– Increase accessibility for children with physical disabilities• Reexamine tracking technologies such as Eye/Gaze trackingwhich may assist in quantifying the engagement of the child
  31. 31. Acknowledgments• Neurobotix would like to thank:– Dr.McManis and Hatch for providingnecessary equipment and support regardingearly childhood education– Dr. Iasemidis and Dr. Vlachos for researchand testing support– Austin Hoggatt for assistance in research,development, testing, data analysis andtroubleshooting– Dr. O’Neal for guidance throughout the project
  32. 32. References• Bremner, F. J., F. Moritz, and V. Benignus. "EEG Correlates of Attention InHumans."Neuropsychologia. 10 (1972): 307-12.• Gomez, Pablo. Power Analysis of Alpha and Beta Waves in EEG Signals toDetermine the Most Likely State of a Subject. Tech. Miami: Florida InternationalUniversity, 2002.• Insel, Thomas. "Autism Prevalence: More Affected or More Detected?" NIMH.National Institutes of Health, 29 Mar. 2012. Web. Oct. 2012.• Niedermeyer, E. "The Normal EEG of the WakingAdult." Electroencephalography: Basic Principles, Clinical Applications and RelatedFields. Baltimore: Lippincott Williams & Wilkins, 1999. 149-173.• Robelledo-Mendez, Genaro, et al. "Assessing NeuroSky’s Usability to DetectAttention Levels in an Assessment Exercise." Human-Computer Interaction 56.10(2009): 149-58.• Tatum, William, IV, and et al. Handbook of EEG Interpretation. N.p.: Demos MedicalLLC, 2008.• Images taken from:–––