BRAIN COMPUTER INTERFACE:BASED SMART LIVING ENVIROMENTAL AUTO-

ADJUSTMENT CONTROL IN UNIVERSAL PLUG AND PLAY HOME NETWORKING

PRESENTED BYANU .N.RAJ

S7 ITROLL : 31

GUIDE: IERIN BABU

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INDRODUCTION

• Biological computer

• Analogues to cpu of a computer

• Neurons :- eclectically excitable cells in brain

• Electroencheplaogram -:detect brain voltage

EEG electrodes NEURON 3

• A brain–computer interface (BCI) -

is a communication system that translates brain-activity into commands for a computer or other devices

• Based on the advantages of –

BCI technique ,this system has integrated it with the environmental control systems in a smart house

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EXISTING SYSTEM

• P300 based BCI system

• Result of decision making

• Used in real time BCI games Eg:-Mind Balance

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EXISTING SYSTEM …..

• Motor imagery BCI

• EEG of motor movements Eg:-Graz BCI

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DRAWBACKS

• Need active metal command.

• Do not adapt to cognitive state.

• Bulky and expensive.

• High power conception.

• Limit flexibility , portability and practicability.

• Messy cables are used.

• lot’s of electrode’s are used.

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PROPOSED SYSTEM

• A brain computer interface –based smart living environment auto adjustment control system (BSLEACS)

• Cost effective, extendable.

• Based on cognitive state.

• Single EEG channel

• Portable signal aqustisition module

• Bluetooth is used.

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WORKING

• The system architecture of BSLEACS consists of 3modules:

1)Wireless physiological signal acquisition module

2)Embedded signal processing module

3)Host system

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WORKING contin……..

1)Wireless physiological signal acquisition module

• It consist of:-

Front –end amplifier

Microprocessor

Wireless transmission unit

• Amplifier unit:- preamplifier , band pass filter , ADC.

• ADC input stored in microprocessor.

• Power line interference is removed .

• Bluetooth is used in wireless module.

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• Li-ion battery is used .

• Work for 33 hours

EEG amplifier& acqutisition

Microprocessorunit

Wireless transmission

Blue tooth

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2)EMBEDDED SIGNAL PROCESSING UNIT

• Real time processing of raw EEG

• Perform the real time cognitive state detection algorithm

• Black fin processor is used.

• UART interface is used.

RAW EEG 13

Wireless unit

UART

Signal processing

RF module /Ethernet

Signal processing module Back fin processor

• Ethernet is used to transfer the command to host system.

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How to decode the EEG signal?

• Theta wave(4-7Hz) and alpha wave( 8-11Hz) are used.

• Reflect the cognitive state.

• EEG of occipital midline is used.

• Discriminating power and high correlation with cognitive

state.

• State of alert to drowsy is detected by increase in alpha

and theta rhythm

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• User’s instantaneous cognitive state is not used.

• Cause discomfort to user.

• Trend of user’s cognitive state is used.

• Average of estimated cognitive state ,previous 10

min .

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Low pass filter

Extract alpha and theta

Build alert model of alpha

Build alert model for theta

Calculate MDA

Calculate MDT

Calculate MDA

Build alert modelCalculate deviation from alert model

RAW EEG

output

How Algorithm works'?

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Flow chart explanation

• High frequency noise removed by filter.

• FFT is used to extract alpha and theta rhythm.

• Alert state is modeled.

• Mahalanobis distance from alert model.

• Drowsy subjects mahalanobis distance increases.

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3)Host system and environment controller

• Processing unit sent command via Ethernet.

• PLC is used for controlling device .

• Ethernet hand over data packet to PLC.

• Control the environment control systems.

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Application in environment control system

• During exercise and working ,alert state

• Alert state ,day lamp is on

• Relaxing and sleeping ,drowsy state

• Drowsy state, day lamp is off

Alert state Drowsy state 21

Advantages

BCI system Mind balance Graz BCI BSLEACS

EEG signal P300 wave Motor imagery Alpha and theta

Channels 2 3 1

Transmission Cable Cable Bluetooth

Power supply power line power line Li-ion battery

Back end signal processing unit

Personal computer Personal computer Embedded signal processing unit

Control mode Active mental command

Active mental command

Adaptation

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Limitation

• Ethical issue prevent its development

• EEG signal is prone to inference.

• There are many chemical process ,can’t be captured.

• Too costly to set up .

• Cannot be used for brain disease people.

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Future scope

• Bionic eye

• Transfer the hearing impulse to brain.

• Robotic assistance to old and disables

• Real time gaming

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Conclusion

• BCI is a high potential idea with high impact on the

society .

• Future developments enable ,complex performance .

• Researchers hope that every thing will be mind

controlled in future.

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References

• www.howstuffswork.com

• www.ieeexplore.com

• www.wekipedia.com• Brain-Computer Interfaces Using Electrocorticographic Signals

Gerwin Schalk, Member, IEEE, and Eric C. Leuthardt,2011

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THANK YOU !

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