Brain Computer Interface in BMI

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Brain Computer Interface in BMI
Ioannis Papavasileiou Computer Science
Engineering Department The University of
Connecticut 371 Fairfield Road, Unit 4155 Storrs,
CT 06269-2155
papabasile_at_engr.uconn.edu
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What is BCI?

• BCI is
• System that allows direct communication pathway
  between human brain and computer
• It consists of data acquisition devices, and
  appropriate algorithms
• How is it used in BMI
• Clinical research
• Disease-condition detection and treatment
• Human computer interfaces for
• Control
• Emotions detection
• Text input - communication

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Research areas involved

• Computer science
• Data mining
• Machine learning
• Human computer interaction
• Neuroscience
• Cognitive science
• Engineering

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Key challenges

• Technology-related
• Sensor quality low SNR
• Supervised learning curse of dimensionality
• System usability
• Real-time constraints
• Non-invasive EEG information transfer rate is
  approx. 1 order of magn. lower
• People-related
• People are not always familiar with technology
• Preparation training phases are not fun!
• Concentration, attention consciousness levels
• Task difficulty

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BCI components

• Data acquisition
• Electroencephalography (EEG)
• Electrical activity recording
• Invasive or not
• Functional Near Infrared Spectroscopy (fNIRS)
• Recording of infrared light reflections of the
  brain
• Functional magnetic resonance imaging (FMRI)
• Detection of changes in blood flow
• Data Analysis
• Data mining machine learning
• Decision making
• Output Control
• HCI

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Typical BCI architecture
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Electroencephalography (EEG)

• What is it
• Recoding of the electrical activity of the brain
• Types
• Invasive
• Non-invasive
• Properties
• High temporal resolution
• Low spatial resolution
• Scalp acts as filter!

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International 10-20 standard

• Electrodes located at the scalp at predefined
  positions
• Number of electrodes can vary

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The EEG waves

• Alpha occipitally
• Beta frontally and parietally
• Theta children, sleeping adults
• Delta infants, sleeping adults

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fMRI

• Functional magnetic resonance imaging
• Fact
• Cerebral blood flow and neuronal activation
  coupled
• Detection of blood flow changes
• Use of magnetic fields
• High spatial resolution
• Low temporal resolution
• Clinical use
• Assess risky brain surgery
• Study brain functions
• Normal
• Diseased
• Injured
• Map functional areas of the brain

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fNIRS

• Functional Near Infrared Spectroscopy
• Project near infrared light into the brain from
  the scalp
• Measure changes in the reflection of the light
  due to
• Oxygen levels associated with brain activity
• Result absorption and scattering of the light
  photons
• Used to build maps of brain activity
• High spatial resolution
• lt1 cm
• Lower temporal resolution
• gt2-5 seconds

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BMI clinical applications

• Diagnose
• Epilepsy seizures
• Brain-death
• Alzheimers disease
• Physical or mental problems
• Study of
• Problems with loss of consciousness
• Schizophrenia (reduced
• Delta waves during sleep)
• Find location of
• Tumor
• Infection
• bleeding

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Sleep disorders mental tasks

• Sleep disorders study
• Insomnia
• Hypersomnia
• Circadian rhythm disorders
• Parasomnia (disruptions in slow sleep waves)
• Mental tasks monitoring
• Mathematical operations
• Counting
• Etc.

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Neurofeedback

• Applications in
• Autistic Spectrum Disorder (ASD)
• Anxiety
• Depression
• Personality
• Mood
• Nervous system
• Self control

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Feedback EEG-BCI architecture
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Typical data analysis process

• Data acquisition and segmentation
• Preprocessing
• Removal of artifacts
• Facial muscle activity
• External sources, like power lines
• Feature extraction
• Typically sliding window
• Time-frequency features
• Latency introduced

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Feature extraction

• Model-based methods
• Require selection of the model order
• FFT (Fast Fourier Transform) based methods
• Apply a smoothing window
• Features used
• Specific frequency band power
• Band-pass filtering and squaring
• Autoregressive spectral analysis
• Many times a feature selection or projection is
  done to reduce the huge feature vectors

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Data Classification

• Typical classifiers used
• Artificial Neural Networks (ANN)
• Linear Discriminant analysis (LDA)
• Support Vector Machines (SVM)
• Bayesian classifier
• Hidden Markov Models (HMM)
• K-nearest neighbor (KNN)
• Parameters for each classifier can affect the
  performance
• of hidden units in ANN
• of supporting vectors for SVMs
• Etc.

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Human computer interaction

• BCIs are considered to be means of communication
  and control for their users
• HCI community defines three types
• Active BCIs
• Consciously controlled by the user
• E.g. sensorimotor imagery (multi-valued control
  signal)
• Reactive BCIs
• Output derived from reaction to external
  stimulation
• Like P300 spellers
• Passive BCIs
• Output is related to arbitrary brain activity
• E.g. memory load, emotional state, surprise, etc.
• Used in assistive technologies and rehabilitation
  therapies

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BCI Assistive Technologies

• Communication systems
• Basic yes/no
• Character spellers
• Virtual keyboards
• Control
• Movement imagination
• Cursor
• Wheelchairs
• Artificial limbs prosthesis
• Automation in smart environments
• Current BCI systems have at most 10-25
  bits/minute maximum information transfer rates
• It can be valuable for those with severe
  disabilities

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P300 spellers

• Most typical reactive BCI
• 3-4 characters / min with 95 success

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P300 wave

• Event related potential (ERP)
• Elicited in the process of decision making
• Occurs when person reacts to stimulus
• Characteristics
• Positive deflection in voltage
• Latency 250 to 500 ms
• Typically 300 ms
• Close to the parietal lobe in the brain
• Averaging over multiple records required

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Other ERP uses

• Lie detection
• Increased legal permissibility
• Compared to other methods
• ERP abnormalities related to conditions such as
• Parkinsons
• Stroke
• Head injuries
• And others
• Typical ERP paradigms
• Event related synchronization (ERS)
• Event related de-synchronization (ERD)

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Other Control BCI paradigms

• Lateralized readiness potential
• Game control
• 12 seconds latency
• Negative shift in EEG develops before actual
  movement onset
• Steady-state visually evoked potentials (SSVEPs)
• Slow cortical potential (SCP)
• Imaged movements affect mu-rhythms
• They shift polarity ( or -) of SCP
• Sensorimotor cortex rhythms (SMR)
• EMG

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SCP SMR vs P300

• Typically SCP and SMR BCIs require significant
  training to gain sufficient control
• In contrast P300 BCIs require less as they record
  response to stimuli
• However, they require some sort of stimuli like
  visual (monitor always in place) or audio
• Also SCP BCIs have longer response times

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Binary speller control

• User imagines movement of cursor
• Typically hand movement
• The goal is to select a character

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Wheel chair control

• All the mentioned BCI paradigms have been applied
  to wheelchair control
• Either using a monitor for feedback
• Or active paradigms as sensorimotor imagery (SMR)
• Similar approaches have been applied to robotics
• Artificial limbs
• etc

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Environment control

• BCIs used by disabled to improve quality of life
• Operation of devices like
• Lights
• TV
• Stereo sets
• Motorized beds
• Doors
• Etc
• Typically use of P300, SMR and EMG related BCIs

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EMG-based human-robot interface example

• Motion prediction based on hand position
• EMG pattern classification as control command
• Combination of both yields motion command to
  prosthetic hand

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Emotions detection

• Use of facial expressions to imply user emotions
• ERD/ERS based BCIs
• Emotional state can change the asymmetry of the
  frontal alpha
• P300 - SSVEP
• Emotional state can change the amplitude of the
  signal from 200ms after stimulus presentation

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BCIs for recreation

• Games
• EPOC headset
• Mindset
• Virtual reality
• Outputs of a BCI are
• Shown virtual environment
• Creative Expression
• Music
• Generated form EEG signals
• Visual art
• Painting for artists who are locked in as a
  result of ALS amyotrophic lateral sclerosis

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Security and EEG

• EEG has been used in user authentication
• Every brain is different
• Different characteristics of EEG waves are used
  in user authentication
• Pros
• User has nothing to remember
• Harmless
• Automatically applied
• Cons
• User has to wear an EEG headset
• Accuracy is still not 100
• Still not used in practice

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Thank you!