This document summarizes a project on developing a brain-computer interface system to help people with disabilities control external devices. It lists the supervisors and team members working on the project. It then outlines the agenda which includes defining the problem, objectives, motivations, system architecture, implementation, and future work. It notes disability statistics in Egypt and the objective is to help people overcome disabilities. The motivations include the technology now being more successful and dealing with new techniques. The system architecture involves acquiring brain signals, preprocessing, feature extraction, classification, and decision steps. The implementation uses an EMOTIV headset and explores preprocessing, feature extraction using wavelets, Fourier transforms and PCA, and classification using neural networks. Future work involves adding more

1. 1

2. Supervisors :
Dr. : Howida AbdEl-fattah
2

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)
3

4. Agenda
• Problem Definition
• Project Objective
• Project Motivation
• System Architecture
• System implementation
• Future work
• Reference
4

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
5

6. Project Objective
Helping people Around the world to
overcome their disabilities and have
a normal life like any other one.
6

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
7

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.
8

9. System architecture
Signal
Preprocessing Feature Extraction
Acquisition
Decision Classification
9

10. System Acquisition
How to explore brain activity?
Invasive Noninvasive
10

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)
11

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
12

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)
15

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)
16

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.
17

18. Multi-Layer Perceptron
18

19. Decision
Decision
19

20. System implementation
Demo
20

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
21

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.
22

23. Questions?
23

24. Thank YOU…
24