Ch.1
Introduction to Brain-Computer
Interfacing
Overview
• Fairytales: translating thoughts into actions without acting
physically.
• Recent BCI technologies made it possible!
• Aims of BIC
– Restore sensory functions
– Restore motor functions
• Application areas: neuroprostheses, computer games, etc.
• Technologies for monitoring brain activity:
electroencephalography (EEG), invasive electrodes,
magnetoencephalography (MEG), positron emission tomography
(PET), functional magnetic resonance imaging (fMRI), optical
imaging (fNIRS)
• Portable and practical: EEG, fNIRS, invasive electrodes
• Technological bottleneck: sensors
• Advanced signal processing and machine learning play a key role.
• Other issues: robustness, online adaptation to nonstationarities,
sensor fusion, classifier and filter parameter tuning
Hans Berger early 1900s
Electromagnetic Fields of Neural Activity
=65m at f = 100Hz
in head tissues
Sensors: average distance to neural generators = 0.15m
 Quasistatic assumption
: “Neglect propagation of EM waves”
EEG
 Hans Berger (1929)
 Reasonably low-cost
 Widely used in clinical practice + Neuropsychology research units
EEG
MicroMed
Electrical
Geodesics
NeuroScan
Magnetoencephalography
(MEG)
• SQUID (Superconducting
QUantum Interference Device)
• Records magnetic fields
produced by electrical
activity in the brain.
• Study neuronal activity by
means of magnetic fields.
• Temporal resolution in msec.
• Spatial resolution in mm,
better than that of EEG.
MEG
 SQUID- James Zimmerman (1968), David Cohen (1972)
 Cost = MRI
 About 50 centers worldwide – still counting . . .
PET
• Positron Emission Tomography (PET) is a technique for
measuring the concentrations of positron-emitting
radioisotopes within the tissue of living subjects. These
measurements are made outside of the living subjects.
PET can be broken down into several steps:
– label a selected compound with a positron- emitting
radionuclide
– administer this compound to the subject of study
– image the distribution of the positron activity as a
function of time by emission tomography
• The main positron- emitting radionuclides used in PET
include Carbon-11, Nitrogen-13, Oxygen-15, and
Fluorine-18, with half-lives of 20 min, 10 min, 2 min, and
110 min respectively.
PET System
PET Images
fMRI
BOLD & fMR Images
fNIRS
NIRS Signals & Images
Approaches to BCI Control
• Two separate approaches, but mostly
mixed of these two.
1) Learning to voluntarily regulate brain
activity by means of neurofeedback and
operant learning principles.
2) Machine learning procedures that enable
the interference of the statistical signature
of specific brain states or intentions within
a calibration session.
• The Biofeedback Approach
– Voluntary control of the brain response
– Biofeedback is a procedure to acquire voluntary
control over the autonomous parameter of the
brain.
– Subjects receive visual, auditory, or tactile
information about their cardiovascular activities
(heart rate, blood pressure), temperature, skin
conductance, muscular activity, electrical brain
activities, blood oxygenation responses.
– Subjects are asked to either increase or
decrease the activity of interest.
– By means of the feedback signal, participants
receive continuous information about the
alteration of the activity.
• The Machine Learning Approach
– Detection of the relevant brain signal
– Training is moved from subjects to learning
algorithm
– Decoding algorithms are individually adapted to the
users that perform the task.
– Learning algorithms require examples from which
they can infer the underlying statistical structure of
the respective brain state.
– Subjects are first required to repeatedly procedure a
certain brain state during a calibration session.
– Machine learning algorithms extract spatiotemporal
blueprints of these brain activities which are used in
subsequent feedback session.
– Challenge is the trial-to-trial variability. Advanced
machine learning techniques are essential.
• Integration of the Two Approaches
– In practice, BCIs will neither rely solely on
feedback learning of uses nor only on
machine learning approaches.
– Co-adaptation of the learning use and
algorithm is inevitable.
– BCI illiterates: typically about 20% of the
users are unable to successfully classify the
brain activation patters.
– Further research work is needed.
Clinical Target Groups
• Individuals in need of a BCI for motor
control and communication
• Examples
– Amyotrophic lateral sclerosis
– Cervical spinal cord injury
– Brain stem stroke
BCI for Healthy Subjects
• Recent interest as a HCI technology
• Addition to keyboard, computer mouse, speech or
gesture recognition devices
• Ch. 23, 24, 25 for first examples
• Brain signals read in real-time on a single trial basis
could provide direct access to human brain states
which can be used to adapt HMI.
• Monitoring tasks such as alertness monitoring,
cognitive workload, alertness, task involvement,
emotion or concentration.
• Current bottlenecks: sensor prices, error rate, price
of EEG. Need fashionable, cheap, contactless EEG
caps.
Brain Pong (Dornhege et al, 2006)
Recording Methods, Paradigms,
and Systems for BCI
• Current BCIs differ in how the neural
activity of the brain is recorded, how
subjects are trained, how the signals are
translated into device commends, which
application is provided to the user.
• Noninvasive Recording Methods for BCI
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–
–
–
Recorded from the Scalp (EEG)
Magnetic Activity of the Brain (MEG)
Blood Oxygen Level Dependent (BOLD, fMRI)
Blood Flow (NIRS)
EEG-BCIs
•
•
•
•
Slow Cortical Potential BCI (SCP-BCI)
Sensorimotor Rhythm BCI (SMR-BCI)
P300 BCI (P300-BCI)
Steady-State Visual Evoked Potential BCI
(SSVEP-BCI)
Generic Noninvasive BCI Setup
Slow Cortical Potentials (SCPs)
SCP-BCI
• SCP-BCI requires users to achieve
voluntary regulation of brain activity.
• The traditional S1-S2 paradigm
– Detection of contingent negative variation
(CNV): a negative SCP shift seen after a
warning stimulus (S1) two to ten seconds
before an imperative stimulus (S2) that
requires users to perform a task.