controlling arm using brain signal using emotive headseat and worked on 3 classes (open arm ,closed arm and closed hand)

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2. Supervisors :
Dr. : Howida AbdEl-fattah
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3. Team members
• Mohamed Magdy Elsayed (CS)
• Mohamed Magdy Abd El-Rheem (CS)
• Mo’men Osama Abd El-Gaffar (CS)
• Mohamed Ahmed El-Sayed (CS)
• Mohamed Hamdy Ibraheem (CS)
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4. Agenda
• Problem Definition
• Project Objective
• Project Motivation
• System Architecture
• System implementation
• Future work
• Reference
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5. Problem Definition
In year 1996 in Egypt:
• 1.6% Lose of one or both arms
• 3.2% Lose of one or both legs
• 18.7% Paralysis total or partial
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6. Project Objective
Helping people Around the world to
overcome their disabilities and have
a normal life like any other one.
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7. Project Motivation
• A lot of people cannot imagine how this system
will be done and used.
• This project not really popular in Egypt “till
now”.
• Recently, intense research has been conducted in
BCI technology
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8. Project Motivation cont.
• And now many projects reach the levels of
success originally touted.
• We will deal with new technology and
implement it by using new techniques.
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9. System architecture
Signal
Preprocessing Feature Extraction
Acquisition
Decision Classification
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10. System Acquisition
How to explore brain activity?
Invasive Noninvasive
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11. EMOTIV Headset
• The EMOTIV Headset (EPOC) has 14 electrodes
(compared to the 19 electrodes of a standard
medical EEG).
• We use only 5 channels (AF3-F7-F3-FC5-P7)
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12. Preprocessing
• there are two purpose for preprocessing
KeepRemove artifacts signals: certain frequency
interest in EEG signals in
band(0.5-45)
Band Pass Filter
Biological Environmental
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13. Feature Extraction Techniques
1.Wavelet transformation (82%)
2.Fourier transformation(73%)
3.PCA (52%) 90
80
70
60
50
40 Accuracy
30
20
10
0
Wavlet Fourier PCA 13

14. Fourier
provides a signal which is localized
only in the Frequency domain.
Features are magnitude values for the
specified spectral range of frequencies
Ex: 1-Range(8-30) = 23 features for each channel
2-Top Ten Frequencies for each channel 14

15. Wavelet packet decomposition WPD:
• Is localized in both time and frequency
•Divided signal into component according to time
•Parameters : according to the required Band and the
sampling rate we select the number of levels for our
WPD
•Features : Mu-Sigma-Min-Max-Epsilon (30 features for
each channel)
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16. Principal Components
Analysis
• It is a way of identifying patterns in data,
and expressing the data in such a way as
to highlight their similarities and
differences
• The other main advantage of PCA is that
once you have found these patterns in
the data, and you compress the data
without much loss of information.
• (5 features for each channel)
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17. Classification
In this step we need to classify the signal to detect
the Arm motion
Neural Networks
• A type of artificial intelligence that
attempts to imitate the way a human
brain works. Rather than using a digital
model.
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18. Multi-Layer Perceptron
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19. Decision
Decision
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20. System implementation
Demo
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21. Future Work
• Add more movements of different parts of the
body
• Get the data from emotive headset to the arm
directly using wireless connection
• Implement the program on a microcontroller
in the arm
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22. References
[1] R. Palaniappan and D. P. Mandic. EEG based biometric framework for automatic identity
verification. Journal of VLSI Signal Processing
Systems, 49(2):243–250, 2007.
[2] R. Palaniappan and K. Ravi. Improving visual evoked potential feature classification for
person recognition using PCA and normalization.
Pattern Recognition Letters, 27(7):726 – 733, 2006.
[3] R. Paranjape, J. Mahovsky, L. Benedicenti, and Z. Koles’. The electroencephalogram as a
biometric. In Canadian Conference on Electrical and Computer Engineering, volume 2,
pages 1363 –1366, 2001.
[4] M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou. Parametric person
identification from the EEG using computational
geometry. volume 2, pages 1005 –1008, Pafos, Cyprus, 1999.
[5] M. Poulos, M. Rangoussi, V. Chrissikopoulos, and A. Evangelou. Person identification based
on parametric processing of the EEG. volume 1, pages 283 –286, Pafos, Cyprus, 1999.
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23. Questions?
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24. Thank YOU…
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