brain computer interface ppt

Brain Computer Interface

By,

Ajay George

S8 - IT

04/08/23 IT Department, JECC

What is BCI

• Direct communication pathway between the brain and an external device

• Reads electrical signals from brain

• Signals translated into a digital form

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History

• Research started from 1970

• BCI Project by Jacques Vidal

• Implanting simple BCI sensors within rats, mice, monkeys, and humans.

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History

• 1990 - implanting an electrode in the motor cortex of a paralyzed patient.

• Makes the patient communicate by moving a cursor.

• 1999 – Trained rats to use their brain signals to move a robotic water-dispensing arm.

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How BCI work

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How BCI work

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How BCI work

• Uses optical nerves for image input

• Camera input directed to brain

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Types of BCI

• Invasive• Partially Invasive• Non-Invasive

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Invasive BCI

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• Targeted for people with paralysis

• Implanted directly into the grey matter

• Produce the highest quality signals

• scar-tissue build-upJens Naumann, a man with acquired blindness, being

interviewed about his vision

Partially Invasive BCI

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• BCI devices are implanted inside the skull

• produce better resolution signals

• lower risk of forming scar-tissue

Cathy Hutchinson, who was one of the first persons to have a

direct connection between her brain and a computer implanted

Non-Invasive BCI

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• Easy to wear• produce poor signal• dispersing the

electromagnetic waves created by the neurons

Recording Domains

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Electrocorticography(ECoG )

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• Pioneered in the early 1950s• Measures the electrical activity of the brain• Taken from beneath the skull• Embeds electrodes in a plastic bag placed

above cortex• A surgical incision is required

MRI technology

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• Uses brain signals to control

• Detects the subject’s brain signals and sends the MRI signals over Ethernet cables, via TCP/IP, to a computer.

MRI technology

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Magnetoencephalography (MEG)

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• Magnetic Field of 10-15 T to 10-13 T

• S.Q.U.I.D Sensors are required

• Shielded room is needed

Electroencephalography (EEG)

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• Recording of electrical activity along the scalp

• Measures voltage fluctuations resulting from ionic current.

• Fine temporal resolution• Ease of use, portable and low set-up cost

Electroencephalography (EEG)

Emotiv Cap, 14 Electordes,

Wireless connection. Commercial BCI from NeuroSky

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Electroencephalography (EEG)

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P300

Electroencephalography (EEG)

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• Described in frequency ranges• Delta ( ) < 4 Hz. Most apparent in deep δ

sleep states.• Theta ( ) waves 4-8 Hz, appear in a relaxed θ

state and during light sleep and meditation.

Electroencephalography (EEG)

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• Alpha ( ) waves 8-12 Hz, associated with αmeditation and relaxation.

• Beta ( ) 13-30 Hz waves, connected to βalertness and focus.

• Gamma ( ) waves > 30 Hz, rγ elated to subjective awareness

Electroencephalography (EEG)

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System Block Diagram

Processes

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• Bandpass Filter - to filter out frequencies that do not fall within the α and β ranges.

• Related to senseorimotor activities

• Common Spatial Patterns (CSP) – enhances the discriminability between classes.

Processes

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Feature Extraction methods used to collect useful vectors

• Log Variance• Power Density Estimation (PSD)• Wavelet Packet Decomposition (WPD)

Processes

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• Principle Component Analysis (PCA) -reduce the dimensionality of the feature vector

• Classification Method - to build classifier which discriminate between labels. Linear Discriminant Analysis (LDA) is used

Electroencephalography (EEG)

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Applications

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• Medicinal• Military• Bioengineering• Brain operated wheelchair• Multimedia and Virtual Reality

Conclusion

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• Enables people to communicate and control appliances with use of brain signals

• Open gates for disabled people.• Development of new brain imagining

techniques• Numerous future applications

Bibliography

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• Toward Inexpensive and Practical Brain Computer Interface by Hamzah S. AlZu’bi Nayel S. Al-Zubi Waleed Al-Nuaimy

• Robot Navigation using Brain-Computer Interfaces by Athanasios Vourvopoulos and Fotis Liarokapis

Bibliography

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• A general framework of Brain-Computer Interface with Visualization and Virtual Reality Feedback by Gufei Sun, Kuangda Li, Xiaoqiang Li, Bofeng Zhang, Shizhong Yuan, Gengfeng Wu

Thank You

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