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

This work by Adil Mehmood Khan explores the application of brain-computer interfaces (BCIs) for enabling communication in patients with paralysis. The commonly used TTD feedback and communication system, adapted from BCI2000, facilitates web surfing and communication through conscious brain activity modulation. It discusses auditory and visual feedback in BCI paradigms, highlights studies on EEG and ECoG signals, and presents comparisons between non-invasive BCI methods. The ultimate goal is to enhance the quality of life for individuals with severe disabilities through advanced technology.

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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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