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8/9/2019 Babiloni Slides
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Brain Computer Interface forcommunication and control
Fabio Babiloni
Dept. Human Physiology and PharmacologyUniversity of Rome, “La Sapienza”
Rome, Italy
IRCCS “Fondazione Santa Lucia”, Rome, Italy
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Human computer interfaces
In the classical Star Wars third movie (the returnof Jedi) Darth Vader reveals a connection between
his neural system and the computer
Today, such high levelof integration betweenman and machine seemsreally yet too far fromthe common practice
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Overview of the presentation
Future trends
Definition of a Brain Computer Interface
Principalneurophysiologicalsignals that can be
used to do the job
The most active
research groups inthe BCI field andtheir achievements
Nicolelis, Nature 20
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Brain-Computer communication
through EEG
“Brain–computer interfaces (BCI’s) give their userscommunication and control channels that do notdepend on the brain’s normal output channels of
peripheral nerves and muscles.”
“A BCI changes the electrophysiological signals frommere reflections of CNS activity into the intendedproduct of the activity: messages and commands
that act on the world”Wolpaw, 2002
Feedback and biological adaptation Nicolelis, Nature 2001
Acquisition
or estimationof thecorticalactivity
Processing andclassification ofcortical signals
Actuation inthe real world
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The most downloaded paperfrom Clinical Neurophysiology
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Variations of EEG waves are
correlated with some mental states
8-12 Hertz,
alpha EEGwaves
8-12 Hertz,mu EEGwaves
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Movement-related thoughts elicited
specific cortical patterns
Several EEG studies havebeen also demonstrated thatimagined movements eliciteddesynchronization patterns
different for right and leftmovement imaginations
Neuroscientific studies withfMRI have demonstrated thatmotor and parietal areas areinvolved in the imagination ofthe limb movements
Imagined left movement Executed left movement
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Motor cortical activity in tetraplegics
Shoam et al. Nature vol 413 2001
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MRPs Right finger movement alpha ERD
A closer look into the brain
dynamics underlying the movementpreparation and execution
From –1 before (movie start) to +0.1 sec post-movement
Where: centro-parietal scalp area
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On the use of neurophysiological signals
to control devicesEEG, EMG, EOG
– Quality of sensors
– SNR (EMG >>10, EEG ≈ 1)
Actuators
Featureextraction
PatternRecognition
-Time-dependent features- Times series values
-Frequency dependent features– AR, FFT, Wavelet
-LDA, MDA-Non linear classifier
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Present-days BCIs
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Threshold classifiers for the Brai
Computer Interface (Tubingen)
Institute of Medical Psychology andBehavioural NeurobiologyDepartment chair: Prof. Niels Birbaumer
Dr. Andrea Kübler -biologist
Nicola Neumann - psychologistSlavica Coric - assistantDr. Thilo Hinterberger - physicistDr. Jochen Kaiser - psychologistDr. Boris Kotchoubey - psychologist, physician
Dr. Jouri Perelmouter - mathematician
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Patient HPS using the
Thought Translation Device
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Present-days BCIs
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Left Right
Unbalance of ERD for imagined
left and right movements
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EEG patterns related tocognitive tasks Power spectrum increase/decrease of EEG data recorded when
subject imagines or performs a movement of his middle finger.
δ
θ
α
β
γ
Babiloni et al., IEEE Tr. Rehab. Eng., 2000
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Brain Computer Inter aces
at the Graz UniversityProf. Gert Pfurtscheller
Mu-rhythms patternrecognition by linear andnon linear classifiers
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The Adaptive Brain Interface
José del R. Millán
Josep MouriñoMarco Franzè
Fabio TopaniAdriano Palenga
Fabrizio Grassi
Maria Grazia Marciani
Donatella Mattia
Febo Cincotti
Fabio Babiloni
Markus VarstaJukka Heikkonen
Kimmo Kaski
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ABI Training
6.40-7.30
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Brain-operated Virtual Keyboard
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A gameapplication
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Finalist to the Descartes prize 2001
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Present-days BCIs
l ’ d h
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Wolpaw’s Wadsworth
Center
Spelling device(2.25)Aid screen
P300 spelling
device
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BCI controlled by
estimated cortical activity
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Future trends: increase awareness
of controlled devices
BCI is a slow communication channel– Best performance with virtual keyboard: 3characters per minute
Need for “smart” devices, e.g.:– T9 programs for SMS on cellular phones– Trajectory aware weelchairs or robotic
arms
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EEG Based BCI inrehabilitationFocus: degree of Autonomy
– Partially restoring the abilities, mostly usingalternative strategies
– Communication aid-> Controlling device
Focus: degree of Functional Recovery
– Tuning of the rehabilitation actions to maximize levelof recovery– Cortical plasticity->Rehabilitation device
F t t d
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Future trends Identification of those signals, whether evoked potentials,
spontaneous rhythms, or neuronal firing rates, that users arebest able to control independent of activity in conventionalmotor output pathways;
Development of training methods for helping users to gainand maintain that control
Delineation of the best algorithms for translating thesesignals into device commands;
Identification and elimination of artifacts such aselectromyographic and electro-oculographic activity;Adoption of precise and objective procedures for evaluating
BCI performance;
Identification of appropriate BCI applications andappropriate matching of applications and usersAttention to factors that affect user acceptance of
augmentative technology, including ease of use, cosmesis, and
provision of those communication and control capacities thatare most important to the user