BCI systems measure brain activity through sensors and use signal processing and machine learning to interpret cognitive states. BCI can be used for communication, control of prosthetics, and other applications. However, challenges include low signal-to-noise ratios, limited transfer rates, and variability between individuals. Adapting BCI systems based on individual user characteristics such as age, physiology, and skills could improve performance by matching technologies to users. More research is needed to understand these relationships.

1. Brain Computer Interfaces
Muhammad Asjad

2. What is BCI?

3. BCI - Definition
• “A system which takes a bio signal measured from a
person and predicts(in real time / on a single-trial
basis) some abstract aspect of the person’s
cognitive state.”
• Types:
o Active BCI
o Reactive BCI
o Passive BCI

4. Brain Activity
10 secs of brain activity

5. Signals and Sensors
• Invasive vs non – invasive.
o Invasive techniques yield better results
o invasive techniques are not usually used by healthy patients
• How to measure brain activity?
o EEG (Electroencephalogram)
o Functional Near –infrared Spectrography (fNIRS)
o Galvanic skin response (GSR)

6. Signals and Sensors
Figure: Brain data visualization
ECG headset

7. BCI is an interdisciplinary problem and borrows from:
• Signal Processing
• Machine Learning and statics
• Computational Intelligence
• Neuroscience
• Cognitive Science
• Problems are similar to Computer Vision, Speech
Recognition, Pattern Recognition, Time- Series
Analysis, Control System & Robotics

8. Applications
• Communication for
seriously disabled
patients (e.g
Tertraplegia, Locked-in
Syndrome)
• Prosthetic Control
• Entertainment(gaming,
thought control)
• Home Automation
• Operator monitoring
(braking intent,
fatigue,forensices etc.)
• Neuroehabilitation

9. Challenges
• Zero false activation rate is difficult to achieve.
• Limited Transfer rates. E.g 10-25bits/min
o neuroprosthesis control, may require higher information transfer rates
• High Signal to noise ratio
o Sensitive measures are hard to obtain.
o Background activity can interfere with relevant brain activity.
o Sophisticated signal processing is required
• Sensor Placement.
o Calibration is required.
• Brain workings are complicated and still not fully understood.
o Neurons are involved in multiple activities at once.
• Processing depends on unkown parameters (person-specific,
task-specific, otherwise variable).
o Cortex is folded differently for every person.
o Universal models don’t exist. i.e functional map is different for individuals.
o Each individual can show different brain activity for similar tasks

10. Adaptive Solutions
• How can we design solutions that more adaptive to the individual
characteristics of the user?
• BCIs is limited by the ability of users to provide distinguishable changes in
their neurophysiological input, also known as BCI literacy.
o Various factors affect this literacy and range from the person’s current fatigue level to physiological
makeup.
• ITF(individual technology fit) can be reflected by the individual’s
performance with the BCI technology.
• A methodology that ties performance to available BCI technologies
based on individual characteristics can greatly expedite the technology-
fit process.
• The extent that technology functionality matches task requirements and
individual abilities [and] is presumed to lead to higher performance.

11. Adaptive Solutions cont.
• identify salient individual user characteristics that
matches with features of brain- computer interface
technologies
• Individuals vary in their characteristics across many
dimensions
o Characteristics are a person’s demographic, physiological, and cognitive
traits.
• Determine effective approaches, paradigms and
heuristics that link individual characteristics to
available technologies.

12. ITF and Technology
features
• individual-technology fit is the extent to which individual
characteristics match with technology features to
enable a person’s control of a technology.
• Technology Characteristics
o Type – Classification of the general mechanism used
o Biorecording Technology - Approach used to record signals from the
participants (e.g., EEG, fNIR, fMRI, GSR).
o Inputs – Placement of sensors/electrodes (e.g., areas over the brain, fingers)
o Neurological Phenomenon – Phenomenon used to control the transducer (i.e.,
phenomena in electrical brain activity, phenomena in blood oxygenation, or
phenomena in skin conductance).
o Feature Extraction/Translation Algorithms – Component that extracts and
translates the signal into a useful control signal

13. Individual Characteristics - Existing Studies
• Little is known about which individual characteristics
best match with particular BCI technologies
• Research by Randolph and Moore Jackson:
o proposed a set of characteristics affecting BCI technology control and
tested them with fNIR- and GSR-based technologies
o age, regular consumption of caffeine, and years of education all
positively correlated with fNIR control
o age, sex, hair and skin color, hair texture, meditation, regular consumption
of alcohol, and video game experience all positively correlated with GSR
control
o interaction of age and hand- and-arm movement predicted modulation
of the mu rhythm in a mu-based BCI

14. Research by Adriane B. Randolph (Kennesaw State University
)• Focused on the mu rhythm(a type of brain signal), is based on
continuous electrical variations in the motor cortex region of the
brain according to real and imagined movement.
• Mu-based BCIs can take advantage of the difference in signal
properties between idle and active imagery within the motor
cortex region of the brain to produce a control signal.
• The proportional difference in signal properties is measured by a
response R-squared value and indicates signal strength or the
degree of modulation a person may induce
• Results:
instrument playing, being on affective drugs, sex, and age
also play a key role in predicting mu rhythm modulation

15. Conclusion
• More research should be done to further
understand the differences between individuals,
technology, and the impacts on BCI design.
• This will allow assistive technology practitioners to
better incorporate information about their users and
refine their design efforts.

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