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fNIRS and Brain Computer Interface for Communication

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LIVE WEBINAR: June 8, 2017

Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states.

Neural activity is accompanied by a hemodynamic (vascular) responses that is sensitive to a host of features of coordinated brain function. Relating these measures to the seemingly endless breadth of human behavior is a principal aim of many scientific investigations. Fortunately, learning, language acquisition, sensory and motor functions, emotion, social interactions, and the influence of a host of disease processes can all be explored from measures of the functional near-infrared spectroscopy (fNIRS) signal. Wearable fNIRS technology exists that is portable, safe and easy to use, resistant to motion artifacts and can be employed in a subjects natural environment.

A promising application for fNIRS is the design of brain-computer interfaces (BCIs) for communication with completely locked-in patients. In the so called ‘locked-in’ state, fully conscious and awake patients are unable to communicate naturally due to severe motor paralysis. These patients are, however, able to modulate their brain activity which can be decoded and understood by exploring the fNIRS signal.

In this exclusive webinar sponsored by NIRx Medical Technologies, experts present the basic principles of fNIRS and BCI, technical setup and guidelines for running a successful fNIRS study and a comparison of fNIRS with other functional neuroimaging methods. Presenters highlight groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in a completely locked-in state. Specifically, Dr. Ujwal Chaudhary (University of Tübingen) shares results of his research with healthy participants and patients with locked-in syndrome due to amyotrophic lateral sclerosis (ALS). Dr. Bettina Sorger (Maastricht University) presents data from a recent study demonstrating the feasibility of a multiple-choice fNIRS-based communication BCI using differently-timed motor imagery as an information-encoding strategy.

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fNIRS and Brain Computer Interface for Communication

  1. 1. fNIRS and Brain Computer Interface for Communication Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states
  2. 2. InsideScientific is an online educational environment designed for life science researchers. Our goal is to aid in the sharing and distribution of scientific information regarding innovative technologies, protocols, research tools and laboratory services. JOIN FOR FREE AT WWW.INSIDESCIENTIFIC.COM
  3. 3. Bettina Sorger, PhD Assistant Professor Leader of Hemodynamic BCI group Cognitive Neuroscience Department Faculty of Psychology and Neuroscience Maastricht University Functional Near-Infrared Spectroscopy (fNIRS): A promising BCI technique ψ
  4. 4. 1. What is a brain-computer interface (BCI)? • definition, methodology, application 2. Functional-neuroimaging methods for BCIs • neuroelectric vs. hemodynamic methods • BCI-relevant characteristics of hemodynamic brain signals 3. Functional near-infrared spectroscopy (fNIRS) • working principles, practical/procedural aspects, information encoding, advantages and limitations 4. A multiple-choice fNIRS-based communication BCI 5. Summary and conclusion Overview of Presentation
  5. 5. 1. What is a brain-computer interface (BCI)?
  6. 6. 1. What is a BCI? Definition and application possibilities A brain-computer interface (BCI) is system which connects a (human or animal) brain with a computer. It allows for controlling a external devices through brain activity, i.e., without having to rely on motor output. BCIs can be used for researching, enhancing, assisting, repairing or replacing cognitive or sensory-motor functions.
  7. 7. 1. What is a BCI? General BCI methodology Wolpaw et al., 2002
  8. 8. 2. Functional-neuroimaging methods for BCIs
  9. 9. 2. Functional-neuroimaging methods for BCIs Electrophysiological vs. hemodynamic methods electrophysiological (direct) methods • Electroencephalography (EEG) • Magnetoencephalography (MEG) • Electrocorticography (ECoG) • Intracortical recordings (ICoR) • Functional near-infrared spectroscopy (fNIRS) • Functional magnetic resonance imaging (fMRI) Figure obtained from here
  10. 10. • Electroencephalography (EEG) • Magnetoencephalography (MEG) • Electrocorticography (ECoG) • Intracortical recordings (ICoR) • Functional near-infrared spectroscopy (fNIRS) • Functional magnetic resonance imaging (fMRI) Figure obtained from here 2. Functional-neuroimaging methods for BCIs Electrophysiological vs. hemodynamic methods hemodynamic (indirect) methods
  11. 11. 2. Functional-neuroimaging methods for BCIs Ratings ranging from highly advantageous (+ + +) to extremely disadvantageous (- - -). Sorger (PhD thesis, 2010) 2. Functional-neuroimaging methods for BCIs Advantages and disadvantages of functional-neuroimaging methods for BCI purposes
  12. 12. 0 3 10 time [s] neuroelectric response hemodynamic response 2. Functional-neuroimaging methods for BCIs 100ms BCI-relevant characteristics of hemodynamic signals (1): delay
  13. 13. BCI-relevant characteristics of hemodynamic signals (2): high single-trial reliability Resting periods (20s) Motor-imagery periods (10s) fMRIresponse fMRIresponse Time 0 10 Time [s] 2. Functional-neuroimaging methods for BCIs
  14. 14. BCI-relevant characteristics of hemodynamic signals (3a): sufficient spatial resolution Hemodynamic (fMRI) brain-activation patterns related to: • Motor imagery • Mental calculation • Inner speech 2. Functional-neuroimaging methods for BCIs
  15. 15. BCI-relevant characteristics of hemodynamic signals (3b): sufficient spatial resolution Motor imagery Mental calculation Inner speech fNIRS-coverage mask 2. Functional-neuroimaging methods for BCIs
  16. 16. 3. Functional near-infrared spectroscopy (fNIRS)
  17. 17. 3. Functional near-infrared spectroscopy (fNIRS) Working principles of fNIRS Red light penetrating in biological tissue Figure obtained from here adapted from Naseer (2013) Head surface Banana-shaped pathway of photonsAbsorption spectra of Hb Figure adapted from here Hb HbO2
  18. 18. Practical/procedural aspects of fNIRS (1) 3. Functional near-infrared spectroscopy (fNIRS) NIRScout (NIRx)
  19. 19. Practical/procedural aspects of fNIRS (2) Source and detector optodes placed on the head surface using an elastic cap Example optodes montage (covering prefrontal and occipital cortex) following the 10-20 EEG system CZ 3. Functional near-infrared spectroscopy (fNIRS) sources detectors
  20. 20. Spatial coverage depending on optode montagefNIRS-coverage mask 3. Functional near-infrared spectroscopy (fNIRS) Practical/procedural aspects of fNIRS (3)
  21. 21. Information encoding in fNIRS-BCIs 3. Functional near-infrared spectroscopy (fNIRS) How can we enable patients to encode different information or intentions into differentiable fNIRS signal?
  22. 22. 3. Functional near-infrared spectroscopy (fNIRS) “yes” “no” “yes” “no” thinking “yes” thinking “no”
  23. 23. 3. Functional near-infrared spectroscopy (fNIRS) “yes” “no” Inner speechMotor imagery
  24. 24. • exploitable fNIRS-signal features (spatial, temporal and magnitudinal) 3. Functional near-infrared spectroscopy (fNIRS) Motor imagery Mental calculation Inner speech Information encoding in fNIRS-BCIs
  25. 25. • exploitable fNIRS-signal features (spatial, temporal and magnitudinal) 3. Functional near-infrared spectroscopy (fNIRS) 20s10s0s fNIRSresponse Information encoding in fNIRS-BCIs Start of encoding time window
  26. 26. • exploitable fNIRS-signal features (spatial, temporal and magnitudinal) 3. Functional near-infrared spectroscopy (fNIRS) fNIRSresponse Information encoding in fNIRS-BCIs
  27. 27. • exploitable fNIRS-signal features (spatial, temporal and magnitudinal) • two general forms information-encoding strategies: - intentionally controlled mental activities 1. higher-order cognitive tasks (e.g., mental calculation) 2. covert language-related tasks (e.g., mental speech, mental singing) 3. mental-imagery tasks (motor, visual, auditory, tactile and emotion imagery) 4. selective-attention tasks (visual, auditory, and tactile attention) - automatic cognitive processing 3. Functional near-infrared spectroscopy (fNIRS) Information encoding in fNIRS-BCIs
  28. 28. • relatively low purchase/operating costs [fMRI] • medium temporal resolution (sample rate: up to 20Hz, but cave hemodynamic delay) [fMRI] • sufficient spatial resolution (1cm³ range) • no vulnerability to electromagnetic environment [EEG, fMRI] • relatively low sensitivity to head motion artifacts [EEG, fMRI] • no inverse problem [EEG] • ease of application [EEG, fMRI] • portability/mobility [fMRI] • harmlessness, non-invasiveness • noiselessness [fMRI] • compatibility with paramagnetic (medical) equipment [fMRI] • measuring both oxygenated and deoxygenated Hb changes ( two dependent variables) [EEG, fMRI] • compatibility with EEG, fMRI, TMS/tCS ( simultaneous measurements possible) 3. Functional near-infrared spectroscopy (fNIRS) Advantages of fNIRS as BCI technique
  29. 29. 3. Functional near-infrared spectroscopy (fNIRS) NIRSport – a mobile fNIRS system
  30. 30. • relatively low purchase/operating costs [fMRI] • medium temporal resolution (sample rate: up to 20Hz, but cave hemodynamic delay) [fMRI] • sufficient spatial resolution (1cm³ range) • no vulnerability to electromagnetic environment [EEG, fMRI] • relatively low sensitivity to head motion artifacts [EEG, fMRI] • no inverse problem [EEG] • ease of application [EEG, fMRI] • portability/mobility [fMRI] • harmlessness, non-invasiveness • noiselessness [fMRI] • compatibility with paramagnetic (medical) equipment [fMRI] • measuring both oxygenated and deoxygenated Hb changes ( two dependent variables) [EEG, fMRI] • compatibility with EEG, fMRI, TMS/tCS ( simultaneous measurements possible) 3. Functional near-infrared spectroscopy (fNIRS) Advantages of fNIRS as BCI technique
  31. 31. Limitations of fNIRS as BCI technique • light scattering/absorption by extracerebral tissue and skin/hair pigmentation • limited depth pervasion (reaching only surface layers of cortex) • still-existing practical limitations 3. Functional near-infrared spectroscopy (fNIRS) Figure adapted from here
  32. 32. 4. A multiple-choice fNIRS-based communication BCI
  33. 33. Overview of existing hemodynamic communication BCIs × 4. A multiple-choice fNIRS-based communication BCI Naito et al. (2007) Gallegos-Ayala et al. (2015) Chaudhary et al. (2017)
  34. 34. Option “D” Option “C” Option “B” Option “A” Answer encoding by exploiting temporal fNIRS signal features 4. A multiple-choice fNIRS-based communication BCI Figure obtained from here A B C D
  35. 35. Instruction: “Please imagine drawing simple geometric figures (such as circles, triangles, cubes, etc.) or small contour drawings (e.g., a butterfly, star, car, tree, boat, or house) with your right hand in a comfortable but consistent speed. Try to imagine using a pen. This might support your imagination”. 4. A multiple-choice fNIRS-based communication BCI Mental drawing
  36. 36. Answer encoding with auditory guidance Question: “What is your favorite color?” 4. A multiple-choice fNIRS-based communication BCI time @20s “Red” “Green” @30s “Black” @40s “Other” @50s “Stop” @60s @0s → 5 repetitions (within 6:05 min) to increase accuracy
  37. 37. Option “Other” Option “Black” Option “Green” Option “Red” 4. A multiple-choice fNIRS-based communication BCI Expected (ideal) fNIRS responses
  38. 38. 4. A multiple-choice fNIRS-based communication BCI General procedure
  39. 39. General procedure 4. A multiple-choice fNIRS-based communication BCI
  40. 40. Equidistant optode positioning on left sensorimotor cortex 4. A multiple-choice fNIRS-based communication BCI
  41. 41. Online Data analysis using Turbo-Satori developed by Rainer Goebel (Brain Innovation B.V., Maastricht, the Netherlands) 4. A multiple-choice fNIRS-based communication BCI Time-course inspection of localizer data for best-channel selection (Goebel & Luehrs, submitted)
  42. 42. 4. A multiple-choice fNIRS-based communication BCI * Best-channel selection based on general linear model (GLM) analysis (channel with maximal mean of oxy- and deoxy-Hb t-values) GLMoutput[y-value] Channel
  43. 43. General procedure 4. A multiple-choice fNIRS-based communication BCI
  44. 44. fMRI data of same participant 4. A multiple-choice fNIRS-based communication BCI Time course inspection of answer-encoding data (oxy-Hb) within best-channel (subject encoded answer option “A”)
  45. 45. for “Red” for “Green” for “Black” for “Other” 4. A multiple-choice fNIRS-based communication BCI Answer decoding: GLM analysis of the best-channel’s time course (using response functions according to the four different answer periods)
  46. 46. 4. A multiple-choice fNIRS-based communication BCI Answer decoding: Plotting GLM results and deriving encoded answer (based on maximal mean of oxy- and deoxy-Hb t-values within selected channel) Example question: “What is your favorite color?” * GLMoutput[y-value] Answer option S8-D8
  47. 47. Answer encoding results 25.0%-97.5% (mean: 62.8%) * * * * * * * * *significant at p<.05 (8 out of 9 participants, ca. 89%) (theoretical) chance level (25%) 37.5%-100% (mean: 76.4%) 4. A multiple-choice fNIRS-based communication BCI 1st decoder option correct 4th decoder option correct 3rd decoder option correct 2nd decoder option correct
  48. 48. 5. Summary and conclusion
  49. 49. ✓ fNIRS constitutes a promising BCI method • non-invasiveness, harmlessness, (repeated/frequent usability) • Portability/mobility, ease of use (applicability) • high-single trial reliability (robustness) • high flexibility (individualization potential ) • relatively low costs ✓ high potential for further improvement of fNIRS methodology (hard- and software)  fNIRS highly suited for clinical and daily-life BCI applications Summary & Conclusions
  50. 50. Acknowledgements Colleagues Rainer Goebel Rico Möckel Lars Riecke PhD students Amaia Benitez-Andonegui Hannah Boeijkens Michael Luehrs Laurien Nagels-Coune Internship students Denizhan Kurban Louisa Gossé
  51. 51. Dedicated Near-Infrared Spectroscopy LEARN MORE Thank you to our event sponsor
  52. 52. Dr. Ujwal Chaudhary Research Group Leader, Institute of Medical Psychology, University of Tübingen Research Fellow WYSS Center for Bio and Neuroengineering, Genèva fNIRS and Brain Computer Interface for Communication
  53. 53. 1. What is Brain Computer Interface (BCI)? 2. BCI for Communication 3. Functional Near Infrared Spectroscopy (fNIRS) • Signal processing 4. fNIRS based BCI • Lifecycle of BCI • Feature selection 5. fNIRS based BCI for Communication • Results from patient population Presentation Agenda
  54. 54. The human being lives according to its communication capacity: loosing the capacity for communication means loosing life. Der Mensch lebt in dem Maße, wie er kommunikationsfähig ist: ist die Kommunikationsfähigkeit vorbei, so ist auch das Leben vorbei. L’uomo vive in base alla sua capacita di communicazione, quando la communicazione e finita finisce anche la vita Ludwig Hohl Communication
  55. 55. • Amyotrophic Lateral Sclerosis • Stroke • Brain Inujry • Muscular dystrophy Loss of Communication Augmentative and Alternative Communication
  56. 56. What is Brain Computer Interface? Revolves around the idea to convert thoughts directly into movement without the detour via the motor system Coined for the first time by French neurophysiologist Jacques Vidal in 1973
  57. 57. Chaudhary et al., Nature Reviews Neurology 2016.
  58. 58. Neuroimaging Modalities 1. Electroencephalography (EEG) 2. Magnetic Encephalography (MEG) 3. Functional Magnetic Resonance Imaging (fMRI) 4. Near Infrared Spectroscopy (NIRS)
  59. 59. 1. Electroencephalography (EEG)
  60. 60. 2. Magnetic Encephalography (MEG)
  61. 61. 3. Functional Magnetic Resonance Imaging (fMRI)
  62. 62. Neuroimaging Modalities 1. Electroencephalography (EEG) 2. Magnetic Encephalography (MEG) 3. Functional Magnetic Resonance Imaging (fMRI) 4. Near Infrared Spectroscopy (NIRS)
  63. 63. 4. Near Infrared Spectroscopy (NIRS) Source Detector
  64. 64. BCI for Communication 1. EEG-BCI • Slow Cortical Potential (SCP)- BCI • Sensorimotor rhythm (SMR)-BCI • P300-BCI 2. fNIRS-BCI Rockstroh et al., 1989; Birbaumer, 1997
  65. 65. Near Infrared Spectroscopy (NIRS) of Brain : Principle Minimally absorbed and Preferentially scattered. Source Detector distance 2.5 – 3.5 cm Optical WindowLight Propagation in Brain Strangman et al. Biol Psychiatry. 52 (2002) 679-693. Source Detector Ting Li et al Journal of Biomedical Optics 16(4), 045001(April 2011)
  66. 66. Three different analysis Analysis of NIRS Signal Activation analysis Connectivity analysis Lateralization analysis
  67. 67. Activation Analysis • The raw optical data is filtered using a band pass filter to remove the signals arising from systemic physiology. • Modified Beer Lambert law • Change in relative concentration of HbO, HbR and HbT (sum of HbO and HbR) as a function of time. • Statistical Analysis (Statistical significance)
  68. 68. Brain Connectivity Analysis • Brain connectivity refers to the pattern of connection and interaction between units of brain. • Forms a distributed network wherein the different regions of the brain communicate with each other. • Three types of brain connectivity. Friston, K.J., 1994. Hum. Brain Mapp. 2, 56–78.
  69. 69. Brain Connectivity Analysis Friston, K.J., 1994. Hum. Brain Mapp. 2, 56–78. 1) Anatomical/Structural connectivity which describe the anatomical links between different units in the brain.
  70. 70. Brain Connectivity Analysis 1) Anatomical/Structural connectivity which describe the anatomical links between different units in the brain. 2) Functional connectivity which describes the temporal correlation among the activity of different neurons. ✓ It measures simultaneous coupling between two time series. ✓ Agnostic to the directional or causal relationship between two time series Friston, K.J., 1994. Hum. Brain Mapp. 2, 56–78.
  71. 71. Brain Connectivity Analysis Friston, K.J., 1994. Hum. Brain Mapp. 2, 56–78. 3) Effective connectivity which describes the causal interaction between distinct units within a nervous system.
  72. 72. Lateralization Analysis Hemispherical dominance of the one cortical region over other during the task or even during the resting state of brain. +ve laterality index = left-side dominance -ve laterality index = right-side dominance ≈ 0 laterality index = no dominance, also termed as bilateral activation. )(HbT)(HbT )(HbT)(HbT L(t) rightleft rightleft tt tt    L (t) - Laterality index HbT (t) – Total hemoglobin
  73. 73. Applications of NIRS Activation Study • Auditory • Audio Visual • Cognition • Motor • Language • Resting state • Somatosensory • Visual Healthy: Adults, Children and Infants. Neurological Disorder: Addiction, Alzheimer, Brain Ischemia, Depression, Epilepsy, Hematoma, Seizure, Schizophrenia, Sleep apnea, Stroke rehabilitation. Subject Groups
  74. 74. 1. What is Brain Computer Interface (BCI)? 2. BCI for Communication 3. Functional Near Infrared Spectroscopy (fNIRS) • Signal processing 4. fNIRS based BCI • Lifecycle of BCI • Feature selection 5. fNIRS based BCI for Communication • Results from patient population Presentation Agenda
  75. 75. BCI for Communication in Locked-in State Patient If we are left alone everything becomes unbearable. Wenn wir allein gelassen werden, ist alles zu viel. Si nos dejan solos, todo esta de mas. Antonio Porchia
  76. 76. Amyotrophic Lateral Sclerosis • ALS: A progressive motor disease • No Treatment • Artificial Respiration • Locked in State (LIS) • Completely Locked in State (CLIS) • Only affecting sensory and cognitive functions to minor degree. • Communication
  77. 77. • Birbaumer et al. Nature (1999) ALS Patient using SCP-BCI • Kübler & Birbaumer (2008) Clin.Neurophysiol. • Birbaumer et al. Current Opinion in Neurology, 2008
  78. 78. ALS Patient using SCP-BCI
  79. 79. • Several techniques for communication in LIS. • Previously trained patient did not transition. • No motor channels left for communication. • Patient in CLIS without previous BCI training • No techniques work. • Extinction of goal directed thinking proposed. • Circadian rhythm disrupted. Transition from LIS to CLIS
  80. 80. BCI for Communication in Completely Locked-in State Patient We conclude that we would remain in a state of anesthesia (numbness) if our perception would not have a particular preference for this or that. Man sieht daraus, dass wir stets in einem Zustande der Betäubung verharren würden, wenn unsere Perception nicht sozusagen eine hervorstechende Eigentümlichkeit und eine bestimmte Vorliebe für dieses oder jenes besässen. Gottfried Wilhelm Leibniz (1646-1716)
  81. 81. No Means of Communication With permission of parents (caretakers)
  82. 82. ALS patient attending to auditory stimuli NIRS optodes + EEG electrodes placed on the motor region of the patient *Patient’s face revealed with permission from primary caretaker Chaudhary et al., PLoS Biology 2017.Chaudhary et al., Nature Reviews Neurology 2016
  83. 83. Semantic Classical Conditioning Thinking YES „Berlin is the Capital of Germany“ Change in fNIRS EEG (EKG) (EOG) Thinking NO „Berlin is the Capital of Spain“ Change in fNIRS EEG (EKG) (EOG) Furdea et al. (2012) J.Neurosc.MethodsDeMassari et al, Brain (2013)
  84. 84. ALS patient attending to auditory stimuli NIRS optodes + EEG electrodes placed on the motor region of the patient Patients are presented 20 personal questions, 10 true and 10 true false in random order in each session Patient thinking “ja,ja,…” (yes) and “Nein,Nein,…” (no) for true and false sentence, respectively for 15 sec. *Patient’s face revealed with permission from primary caretaker Chaudhary et al., PLoS Biology 2017.Chaudhary et al., Nature Reviews Neurology 2016
  85. 85. NIRS Signal for Yes and No EEG Signal for Yes and No NIRS Signal Acquisition System EEG Signal Acquisition System ALS patient attending to auditory stimuli NIRS optodes + EEG electrodes placed on the motor region of the patient Patients are presented 20 personal questions, 10 true and 10 true false in random order in each session Patient thinking “ja,ja,…” (yes) and “Nein,Nein,…” (no) for true and false sentence, respectively for 15 sec. *Patient’s face revealed with permission from primary caretaker Chaudhary et al., PLoS Biology 2017.Chaudhary et al., Nature Reviews Neurology 2016
  86. 86. NIRS Signal for Yes and No EEG Signal for Yes and No NIRS Signal Acquisition System EEG Signal Acquisition System ALS patient attending to auditory stimuli NIRS optodes + EEG electrodes placed on the motor region of the patient Patients are presented 20 personal questions, 10 true and 10 true false in random order in each session Patient thinking “ja,ja,…” (yes) and “Nein,Nein,…” (no) for true and false sentence, respectively for 15 sec. Sleep Monitoring and offline signal classification *Patient’s face revealed with permission from primary caretaker Chaudhary et al., PLoS Biology 2017.Chaudhary et al., Nature Reviews Neurology 2016
  87. 87. NIRS Signal for Yes and No EEG Signal for Yes and No NIRS Signal Acquisition System EEG Signal Acquisition System Audio Feedback ALS patient attending to auditory stimuli NIRS optodes + EEG electrodes placed on the motor region of the patient Feature Extraction Changes in oxy or de-oxy hemoglobin for Yes and No Train the NIRS SVM classifer Patients are presented 20 personal questions, 10 true and 10 true false in random order in each session Patient thinking “ja,ja,…” (yes) and “Nein,Nein,…” (no) for true and false sentence, respectively for 15 sec. Sleep Monitoring and offline signal classification *Patient’s face revealed with permission from primary caretaker Chaudhary et al., PLoS Biology 2017.Chaudhary et al., Nature Reviews Neurology 2016
  88. 88. Hemodynamic Change of “yes” and “no” Response
  89. 89. Feature Extraction & Machine Learning Linear and non linear classifier: Support vector machine, Linear Discriminant Analysis, and others. Features: Mean Slope Variance Moving average Root mean square Skewness
  90. 90. Classification Accuracy: Patient F 30 40 50 60 70 80 90 100 1 2 3 4 5 6 7 8 9 10 11 12 13 14 30 40 50 60 70 80 90 100 Mean classification accuracy (CA) Training session chance level threshold Feedback session CA Open question session CA Feedback session chance level threshold + Classificationaccuracy(%) 30 40 50 60 70 80 90 100 Classificationaccuracy(%) B. EEG classification accuracy Number of days A. fNIRS classification accuracy 1 2 3 4 5 6 7 8 9 10 11 12 13 14 C. EOG classification accuracy Classificationaccuracy(%)
  91. 91. 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 A. Training Sessions’ ROC Curve False Positive Rate TruePositiveRate TruePositiveRate Random Above Chance Random Below Chance Above Chance Below Chance B. Feedback Sessions’ ROC Curve (0.71,0.33) (0.68,0.28) False Positive Rate Receiver Operating Characteristic Curve: Patient F Total Sentences Predicted True Predicted False True Sentence True Positive False Negative False Sentence False Positive True Negative True positive rate = Ʃ 𝑇𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 Ʃ 𝑇𝑟𝑢𝑒 𝑆𝑒𝑛𝑡𝑒𝑛𝑐𝑒𝑠 False positive rate = Ʃ 𝐹𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 Ʃ 𝐹𝑎𝑙𝑠𝑒 𝑆𝑒𝑛𝑡𝑒𝑛𝑐𝑒𝑠 Where… and
  92. 92. Feedback Sessions With permission of parents (caretakers)
  93. 93. With permission of parents (caretakers) Open Question Sessions
  94. 94. • Numerous studies on able bodied individual • Limited controlled, systemic clinical trial on patient population. • fNIRS based BCI for Communication in CLIS • BCI for rehabilitation in stroke • Controlled clinical study in patient population • BCI for motor control in stroke, tetraplegia and Spinal cord Injury. Conclusion
  95. 95. Niels Birbaumer Aygul Rakihmkulova Azim Maleksahi Alessandro Tonin Alberto Lucas Stefano Silvoni WYSS Center John Donoghue Ali Zaidi Thank you!
  96. 96. Thank You Dr. Bettina Sorger, Assistant Professor, Dept. of Cognitive Neuroscience Maastricht University b.sorger@maastrichtuniversity.nl Dr. Ujwal Chaudhary, Research Group Leader, Institute of Medical Psychology, University of Tübingen uchau001@fiu.edu For additional information on the products and applications presented during this webinar please visit www.nirx.net

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