The document discusses brain-computer interfaces (BCI). It describes the challenges in BCI including low signal strength, data transfer rate, and error rate. It outlines the different types of BCI - invasive, partially invasive, and non-invasive - and the acquisition techniques used. The document also discusses BCI signal types, applications such as assisting disabled individuals, and the advantages and disadvantages of BCI technology.

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Department of Physical Science and Technology
Faculty of Applied Sciences
Sabaragamuwa University of Sri Lanka
BRAIN COMPUTER INTERFACE

2. Content
 Introduction
 Challenges in BCI
 BCI System
 How BCI works
 BCI Types
 Invasive BCI Acquisition Techniques
 Partially Invasive BCI Acquisition Techniques
 Non Invasive BCI Acquisition Techniques
 BCI Signal Types
 Classification of BCI Signals
 Applications of BCI
 Advantages of BCI
 Disadvantages of BCI
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3. Introduction
 Brain Computer Interface is a direct
connection between computer and human
brain.
 The BCI can lead to many applications
especially for disabled person.
 BCI applications utilize the brain and its
nervous system functions.
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4. Introduction
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5. Challenges in BCI
 Low BCI signal strength
 Data transfer rate
 High error rate
 Inaccurate signal classification
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6. BCI System
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7. How BCI works
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9. BCI Types
 Invasive BCI acquisition techniques
 Partially Invasive BCI acquisition
techniques
 Non Invasive BCI acquisition
techniques
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10. Invasive BCI Acquisition
Techniques
 Invasive BCI devices have to be used to capture
brain signals.
 Invasive BCI devices are inserted directly into the
human brain by a critical surgery.
 These devices have
the highest quality of
human brain signals.
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11. Partially Invasive BCI
Acquisition Techniques
 Partially Invasive BCI devices are implanted
inside the skull but rest outside the brain.
 Signal strength using this type of BCI is bit
weaker.
 Partially Invasive BCIs
have less risk of scar
tissue formation.
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12. Non Invasive BCI
Acquisition Techniques
 Non Invasive BCI has the least signal clarity
when it comes to communicating with the
brain.
 It has less risk than
the above two
methods.
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13. BCI Signal Types
 There are a plethora of signals, which can be
used for BCI.
 These signals are divide into two classes:
spikes and field potentials.
 Classification of EEG signals:
◦ Delta
◦ Theta
◦ Alpha
◦ Beta
◦ Gamma
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14. Classification of BCI Signals
 Delta signal
 Theta signal
 Alpha signal
 Beta signal
 Gamma signal
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15. Applications of BCI
 Provide disabled people with
communication, environment control, and
movement restoration.
 Provide enhanced control of devices such
as wheelchairs, vehicles, or assistance
robots for people with disabilities.
 Provide additional channel of control in
computer games.
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16. Applications of BCI
 Monitor attention in long-distance drivers
or aircraft pilots, send out alert and
warning for aircraft pilots.
 Develop intelligent relaxation devices.
 Control robots that function in dangerous
or inhospitable situations.
 “Google Search” through brain.
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17. Projects
• Brain Gate
• Australian Bionic Eye
• Honda Asimo Control
• Gaming Control
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18. Honda Asimo Control
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19. Bionic Eye
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21. Advantages of BCI
 Allow paralyzed people to control
prosthetic limbs with their mind.
 Transmit visual images to mind of blind
person.
 Transmit auditory data to mind of deaf
person.
 Allow gamers to control video games with
their mind.
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22. Disadvantages of BCI
 The brain is incredibly complex.
 Signal is weak and prone to interference.
 The equipment is less than portable.
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23. Future??
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24. Conclusion
 BCI is an advancing technology promising paradigm
shift in areas like Machine Control, Human reality and
etc. So, it’s potentially high impact technology.
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25. References
 Melody, M.: Real-world applications for brain-computer
interface technology. IEEE Trans. Neural Syst. Rehabil.
Eng. 11(2), 162–165 (2003)
 Pei, X., Barbour, D., Leuthardt, E., Schalk, G.: Decoding
vowels and consonants in spoken and imagined words
using electrocorticographic signals in humans. J.
Neural. Eng. 8(4), 1–11 (2011)
 Azar, A.T., Balas, V.E., Olariu, T.: Classification of EEG-
based brain-computer interfaces. advanced intelligent
computational technologies and decision support
systems. Stud. Comput. Intell. 486, 97 –106 (2014)
 Buckner, R., Andrews-Hanna, J., Schacter, D.: The
brain’s default network: anatomy, function, and
relevance to disease. Ann. N. Y. Acad. Sci. 1124,1 –38
(2008)
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26. Questions ??
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