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Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach

Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach. By Adil Mehmood Khan. TTD Feedback and Communication System. The current version of TTD software is derived from BCI2000 standard. TTD Software. Data acquisition and storage.

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Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach

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  1. Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan

  2. TTD Feedback and Communication System • The current version of TTD software is derived from BCI2000 standard

  3. TTD Software Data acquisition and storage Online signal processing Classification Feedback and application interface

  4. Spelling by Brain-Computer Communication

  5. Contents ● Web surfing with BCI ● Auditory-controlled BCI ● Visual and auditory feedback comparison ● BCI using ECoG ● Comparison of non-invasive BCI approaches

  6. Brain Controlled Web Surfing • ● Allow patients to surf the web by • concious changes of brain activity • ● Enables a completely paralyzed patient to participate • in the broad portion of life reflected by the WWW. • ● History of providing WWW access to ALS patients dates back to 1999 • TTD was used to operate a standard web browser, i.e. Descartes • ● Descartes was controlled by binary decisions • ● Services provided • Writing letters, writing emails, and surfing the web.

  7. Setup of EEG-controlled web brwoser “Descartes“

  8. Web surfing with “Descartes“ - A ● Patient views a list of predefined WebPages. ● Each webpage is offered successively at the bottom of the screen for selection. ● Page selection through positive SCPs whereas page rejection by negative SCPs.

  9. Web surfing with “Descartes“ - B ● Page loaded after its selection and shown for a predefined period of time.

  10. Web surfing with “Descartes“ - C ● The links on the previous page are offered alphabetically as a dichotomous tree . ● Subject will select or reject each item by regulating SCPs

  11. “Nessi“ – An Improved Graphical Brain-Controllable Browser

  12. BCI-software communication with Nessi

  13. Nessi‘s email interface

  14. Nessi‘s virtual keyboard

  15. An Auditory–Controlled BCI • ● Feedback: • Visual • Auditory • High pitch tones indicate cortical negativity • Low pitch tones indicate cortical positivity • Hybrid (Visual and Auditory)

  16. Auditory–Stimulation in EEG

  17. Auditory–Stimulation in EEG

  18. Auditory–Stimulation in EEG

  19. Auditory–Stimulation in EEG

  20. An Auditory–Controlled BCI: Paradigms

  21. Comparison between Visual and Auditory Feedback

  22. Functional MRI and BCI ● BCI combined with FMRI to uncover relevant areas of brain activation during regulation of SCPs. ● EEG from 12 healthy subjects was recorded inside an MRI scanner while they regulate their SCPs. ● Successful positive SCP shift was related to an increase of blood oxygen level dependent (BOLD) in the anterior basal ganglia. ● While negativity was related to an increased BOLD in the thalamus.

  23. SVM Classification of Autoregressive Coefficients: • ● In contrast to SCPs: • Frequency range below 1Hz • Classified according to their time domain representation • ● EEG correlates of an imagined-movement as best represented by oscillatory features • of higher frequencies, i.e. 8-15 and 20-30 Hz • Desynchronization of μ–rhythm over motor areas. • ● Coefficients of a fitted autoregressive (AR) model were used to realize this • phenomena. • ● SVM was them employed for the classification of these AR coefficients.

  24. SVM Classification of Autoregressive Coefficients:

  25. BCI using ECoG signals: • ● EEG: • Limited signal-to-noise ratio • Low frequency range • ● Invasive ECoG signals: • Broader frequency range (0.016 to 300 Hz) • Increased signal-to-noise ratio • 3 out of 5 epilepsy patients were able to spell their names within only one or two training sessions. • ● ECoG signals were derived from a 64-electrode grid placed over motor-related areas. • ● Imagery of finger or tongue movements was classified with SVM classification of • AR coefficients.

  26. BCI using ECoG signals:

  27. Comparison of Noninvasive Input Signals for BCI • ● Noninvasive BCI: • Sensorimotor rhythms (SMR) • Slow cortical potentials (SCPs) • P300 • ● Extensively studied in healthy participants and to a lesser extent in patients. • ● For this reason SCP-, SMR-, and P300-based BCIs were compared for free spelling.

  28. Background Information for All Patients

  29. Comparison Study • ● SCPs: • None of the seven patients showed sufficient performance after 20 sessions. • ● SMR • Half the patients showed an accuracy ranging from 71 to 81 %. • ● P300 • Performance ranged from 31.7 to 86.3 %

  30. Thanks

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