Presentation on theme: "Brain-Computer Interfaces for Communication in Paralysis: A Clinical Experimental Approach By Adil Mehmood Khan."— Presentation transcript:
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
Data acquisition and storage Online signal processing Classification Feedback and application interface TTD Software
Spelling by Brain-Computer Communication
Contents ● Web surfing with BCI ● Auditory-controlled BCI ● Visual and auditory feedback comparison ● BCI using ECoG ● Comparison of non-invasive BCI approaches
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 s, and surfing the web.
Setup of EEG-controlled web brwoser “Descartes“
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.
Web surfing with “Descartes“ - B ● Page loaded after its selection and shown for a predefined period of time.
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
“Nessi“ – An Improved Graphical Brain-Controllable Browser
BCI-software communication with Nessi
Nessi‘s virtual keyboard
An Auditory–Controlled BCI ● Feedback: Visual Auditory High pitch tones indicate cortical negativity Low pitch tones indicate cortical positivity Hybrid (Visual and Auditory)
Auditory–Stimulation in EEG
An Auditory–Controlled BCI: Paradigms
Comparison between Visual and Auditory Feedback
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.
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 and 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.
SVM Classification of Autoregressive Coefficients:
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.
BCI using ECoG signals:
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.
Background Information for All Patients
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 %