sizz arians
TRANSCRIPT
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By,
Ramya C S (10LEE02)
Sakthivel A (10LEE03)
Gayathri N (10LEE05)
Kangaraju P (10LEE09)
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OBJECTIVE
21 million people in India
2.1% of the total population
Immobility totals to 61,05,477
Unable to execute their daily chores
Develop a prosthetic model
Utilises the brain signals to activate
Replace the impaired part
Model a cost effective solution
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EXISTING TECHNIQUES
A testing of brain activated prosthetic hand used to pick
a cup was done at a cost of Rs.5,00,000 in US
Testing of neuron activated daily chores is performed and
research is carried on to improve it
A lower extremity prosthesis (leg) can minimum cost to
Rs.2,90,000
An upper extremity device (arm) can range from
Rs.1,34,000
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BCI &UNITS OF PROJECT
Brain Computing Interface (BCI) :System able to detect and interpret the mental activity
Changes it to computer interpretable signals
Activities completed without using muscular movementUnits of project:
Brain signal capturing unit
Signal processing unitController unit
Actuator and hand unit
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BASIC CONCEPTS OF EEG CAPTURING
Brain signals are of Delta, Theta,Alpha, Beta and Gama form
Application of DCT and
Butterworth filtersOriginal signal is multiplied with
window function and transform is
computed for each segmentSize of wavelet depends on
frequency components used in the
series
EEG CAPTURING
NORMALISATIO
N
SEGMENT DETECTION
FEATURE
EXTRACTION
CLASSIFIER
EEG
OUTPUT
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BLOCK DIAGRAMBRAIN
ELECTRODES (EEG)
SIGNAL
S
STORAG
E
ENERGISE
ACTUATO
R UNITS
SIGNAL TOCONTROLLER
UNIT
SIGNAL
COMPOSING
UNIT
FINAL
THOUG
HT
PROGRAMSELECTION
BASED ON
SIGNAL
PATTERN
WAIT TILLMOVEMENT
IS
COMPLETED
SIGNALS
SIGNALCOMPOSING
UNIT
CHECK
FOR
MATCHIN
G
AA
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SIGNAL CONDITIONING UNIT
Signals are captured using EEG capSignals filtered to eliminate noises and
other signals
To improve the signal qualityconditioning is done
Patternisation is done to segregate the
signals based on the output to beinitiated
Patternised data is compressed to
provide minimum storage area
EEG SIGNAL
FROM BRAIN
CONDITIONING
PATTERNISE
COMPRESSION
CONDITIONED
BRAIN SIGNAL
FILTERING
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SCHEDULE OF WORKJune : Selection of topic and basic feasibility of project was
analyzed
July : Theoretical study of the project
August : Software simulation of the project
September : Designing of artificial hand
October : Testing the working of hand
December : Real time brain signals capturing and its
processing
January : Integrating brain signals and artificial hand
February : Testing and debugging the project
March : Final makeover of the project and report formulation
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CONCLUSION
The various project development techniques and study of
the existing methodology of the project has been done. The
theoretical concepts related to the project has beencollected. A software necessary for processing the brain
signals has been studied and work is to be proceeded by
analyzing the brain signal data set using the software andfilters for segregating thoughts are to be designed and
constructed.
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REFERENCES
Onur Varol, Mustek Erhan Yalim, Ram EEG dataclassification and applications using SVM, Istanbul
Technical University, 2010
Wenjie Xu, Cuntai Guan, High accuracy classification
of EEG signals , Jiankang Wu-Institute of Infocomm
Research
Lebang Due, Mohd Syaifuddin, Designingand degrees
of freedom humanoid roboticarm
www.bci2000.org