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

30
Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan

Transcript of Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil...

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

Brain-Computer Interfaces for Communication in Paralysis: A Clinical

Experimental Approach

ByAdil Mehmood Khan

Page 2: 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

Page 3: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Data acquisition and storage

Online signal processing

Classification

Feedback and application interface

TTD Software

Page 4: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Spelling by Brain-Computer Communication

Page 5: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Contents

● Web surfing with BCI

● Auditory-controlled BCI

● Visual and auditory feedback comparison

● BCI using ECoG

● Comparison of non-invasive BCI approaches

Page 6: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

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.

Page 7: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Setup of EEG-controlled web brwoser “Descartes“

Page 8: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

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.

Page 9: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Web surfing with “Descartes“ - B

● Page loaded after its selection and shown for a predefined period of time.

Page 10: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

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

Page 11: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

“Nessi“ – An Improved Graphical Brain-Controllable Browser

Page 12: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

BCI-software communication with Nessi

Page 13: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Nessi‘s email interface

Page 14: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Nessi‘s virtual keyboard

Page 15: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

An Auditory–Controlled BCI

● Feedback:•Visual•Auditory

•High pitch tones indicate cortical negativity•Low pitch tones indicate cortical positivity

•Hybrid (Visual and Auditory)

Page 16: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Auditory–Stimulation in EEG

Page 17: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Auditory–Stimulation in EEG

Page 18: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Auditory–Stimulation in EEG

Page 19: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Auditory–Stimulation in EEG

Page 20: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

An Auditory–Controlled BCI: Paradigms

Page 21: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Comparison between Visual and Auditory Feedback

Page 22: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

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.

Page 23: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

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.

Page 24: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

SVM Classification of Autoregressive Coefficients:

Page 25: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

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.

Page 26: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

BCI using ECoG signals:

Page 27: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

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.

Page 28: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Background Information for All Patients

Page 29: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

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 %

Page 30: Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan.

Thanks