Future Work :) We’re in the future already
My Dearest Team 
,
I don’t know what made me do this but I simply love this project
I finished one item in the future work which was integrating with the Emotiv SDK and I discovered that it was much easier than we expected… 
:(
However, here’s a screenshot (Can’t believe i’m doing this again 
)
C2M Future work
This points we stated as our future work for Cap2Monitor for further development
- Employing more techniques, ex: Mu Rythem, wink right, wink left, etc.
- Extending our application that it won’t only include desktop control ability but also environmental control capabilities. [ex: Smart House Control, Robot Control, etc]
- Emotiv systems have developed a new headset for human computer interaction. Developing Cap2Monitor SDK to be compatible with the Emotiv head set based on the latest development in neuro-technology.
Finally C2M is Done!
SSVEP Last Results isA :)
SSVEP Results
=============
1. 7 Hz:
0.78947368421052631578947368421053
0.84615384615384615384615384615385
0.83333333333333333333333333333333
0.75757575757575757575757575757576
0.83333333333333333333333333333333
Average: 81.176
2. 15 Hz:
0.72222222222222222222222222222222
0.66666666666666666666666666666667
0.78125
0.93333333333333333333333333333333
0.73076923076923076923076923076923
Average: 76.62
3. 10 Hz:
76.9 %
0.9375
0.85714285714285714285714285714286
0.89473684210526315789473684210526
0.90909090909090909090909090909091
Average: 87.364
Average Performance: 81.72 = 82 %
MABROOOOOOOOOOOOOOOOOOOOOOOOOOOOK
Sa,
nazran li enni mab7otish 3al blog 5eer kol 5abar sa3eed w mofre7
Guys .. 7dl we hv finished el application bel space w el backspace w kol el aflam di
w el double click .. w 3amlna kaman el look down yi swap..
na2is bs isA el test case w el demo video …
da3watko .. SALAM
Latest News
Dearest Team,
I’ve been invisible for the past few days I know but here’re the results I came up with:
1. For the integration task, I almost finished the integration of the application and I found huge problems in all the algorithms ( Thank god for introducing such noisy data that saleh recorded ).
* For the Blink detection algorithm, now it’s working very fine I guess on all of our datasets.
* And the same for Look down detection algorithm.
* For SSVEP, here are the latest ONLINE results:
Online SSVEP Results
====================
With Threshold = 111
1. Window Size = 10
a. 7 Hz ==> 73 % , 82.8 %
b. 7 Hz (2) ==> 77.16 %
c. 7 Hz (3) ==> 59.8 %
d. 7 Hz (4) ==> 75.4 %
e. 10 Hz (2) ==> 59.7 %
f. 10 Hz ==> 69.5 %
2. Window Size = 9
a. 7 Hz ==> 74.6 %, 78.8 %, 81.5 %
b. 10 Hz ==> 64.6 %, 80 %, 66.3 %
c. 15 Hz ==> 49 %, 44 %, 60.7 %, 65.8 %, 51.7 %
After Lifting up the threshold to 200
1. Window size = 9
a. 7 Hz ==> 96 %, 92.156 %,81.4 %, 97 %
b. 10 Hz ==> 87.7 %, 94 %, 89.5 %,96.6 %
c. 15 Hz ==> 71.4 %, 83.3 %,78.5 %, 85 %, 80 %, 86.6 %, 87 %
You may notice that when I raised the threshold, the results became more interesting except for the 15 Hz that is a bit lower than the other 2 frequencies.
Another thing, we need to record our own data as the past recordings of saleh never raised even a small peak anywhere .. And also we don’t have datasets for the 19 Hz frequency which represents a direction in our cursor movement application.
About the application, It’s now integrated and can recognize blinks,lookdowns and SSVEP stimuli in parallel.
That’s all I had to say
Best Regards,
Hani Amr
Documentation Structure
1. Abstract [Menna]
2. Acknowledgment
3. Table of Contents
3.1. List of Figures
3.2. List of Tables
3.3. Introduction [Omar]
3.3.1. Motivation [Omar]
3.3.2. Problem Definition [Omar]
3.3.3. Project Objective [Omar]
3.3.4. Project Benefits [Removed]
3.3.5. System Description [Removed]
3.4. C2M Analysis
3.4.1. Vision and Scope [Basma]
3.4.1.1. Business Requirements [Basma]
3.4.1.2. Vision of Solution [Basma]
3.4.1.3. Scope and Limitation [Basma]
3.4.1.4. Business Context [Basma]
3.4.2. Software Requirements Specifications [Basma]
3.4.2.1. Purpose and Scope [Basma]
3.4.2.2. Overall description [Basma]
3.4.2.3. Specific Requirements [SW/HW] [Basma]
3.5. Biological and Scientific Background [Menna]
3.5.1. The Human Brain [The brain structure “motor system] [Menna]
3.5.2. Brain Computer Interface ] [Menna]
3.5.3. BCI Applications [Menna]
3.5.4. BCI Categories [Menna]
3.5.4.1. Self regulatory activity [Menna]
3.5.4.2. Event related potential [Menna]
3.6. BCI Techniques
3.6.1. Eye Blinking and Movement [Menna]
3.6.2. Mu Rhythm [Omar]
3.6.3. SSVEP [Basma]
3.6.4. P300 [Menna]
3.7. C2M Design [Omar & Menna]
3.8. C2M Implementation [Hani & Saleh]
3.9. C2M Integration
3.10. C2M Conclusion and Future work
4. References
5. Acronym
6. About C2M Team members
A couple of toys and we’re ready to rock !
Dears,
The post title is weird I know . So, Here’re the final project tasks. Please reply on the mail sent with the task(s) you’ll be participating in isA beside points that are obligatory.
Agenda:
=======
1. Having a good lunch together (Obligatory )
2. Emotiv Issues
a. Try to order the Development Headset
b. Try to use the SDK Lite
3. Documentation (Obligatory)
4. Combining the SSVEP and Blink applications
a. Requires cleaning the last recorded data and testing it
b. Integrate the two applications
5. Preparing for the Final Seminar (Obligatory)
6. Start implementing the P300 Technique
Documentation – Structure
Hello Dears,
Here are some guide points to write our documentation ..
- Introduction (usually discusses why you picked the project, problem definition, how it solves it, the need + how u planned the work + how the documentation is gonna go on)
- Design (you define the design and architecture of the project, include any design documents/diagrams you can come up with)
- Implementation (most probably that’s a few chapters, we wrote every module as a separate chapter since the overall was defined at the beginning)
- Results (what did you achieve, challenges and problems faced, results, graphs, demos, snapshots, etc)
- Related Work (with comparison), Future Work, Appendices (if u need any – extra explanation, important code references, scientific explanation, etc) and References
What i’ve done so far
My Dear Team ,
This is my first post, srry for being late , my progress will be handled as subjects :
Subject One : SSVEP experiment
- SSVEP stands for Steady State Visually Evoked Potentials, they are signals that are natural response to visual stimulation at specific frequencies. When the retina is excited by a visual stimulus ranging from 3.5 to 70 Hz, the brain generates electrical activity at the same (or multiples fo) the visual stimulus.We choose this technique due to the excellent signal to noise ratio and relative immunity to artifacts. SSVEPs provide means to characterize preferred frequencies of neo cortical dynamic progress regardless its high transfer rate.
- 
Four small check boards flickering at different but fixed frequencies move along with a Navigated car . The subject is able to control the movement
of the car by focusing his or her attention on a specific check board , SSVEP’s are generated ,we can realize the check board his gazing at using one of the detection techniques and eventually performing the required navigation.
-Frequency Detection Techniques I’ve learned about are the CCA and the PRSA :
PRSA (research only) : Used with high frequencies starting from 43 to 46 hz , it basically quantifies the quasi periodic Oscillations (have nature of periodic signals) masked by the non stationary nature of composite signals and noise , it basically compresses the signal into a much shorter sequence, keeping all relevant quasi periodic s but eliminating non stationaries , artifacts and noise .
CCA , it is used in frequency recognition of multiple channel signals , the unkown signals are compared against known templates and their frequencies are recognized according to the resulting highest correaltion coefficient , how does that work, it searches for the highest correlation coefficients between two sets of variables which have a certain correaltion , one set conatins signals with frequencies similar to those of the flickering objects and the other conatins the generated SSVEP’s, by calculating the highest correlation coefficient we can detect the SSVEP frequency and accordingly determine the subjects intended target.
I tried to apply this technique on signal data and generated stimulus frequencies using the mat lab and the results were inconsistent and independent, the highest coefficient was way below the expected results even after outlier removal functions which may have affected the resulting coefficients in addition to its low performance in C# integration , these reasons made this technique not suitable and made us use the Fourier instead ..
Subject 2 : 3D face
The blink detection application made by Hani and Saleh , needed an animated face rather than the prototype 2d face integrated with it , by creating a WPF application and integrating a zam3d animated 3dface with expression blend producing an xaml code places n an wpf window we were able to create a 3d environment , when a blink is detected a story board is triggered (the face animation which is the blink) , the face needs some modifications and a look up and look down animation as well , and thought of making him track the cursor if possible..
Subject Three : P300 Speller
Menna needed a P3oo speller for the P3oo technique , its a 6*6 matrix containing the alphabetic letters and numbers fom 0 to 9 , it’s main idea is that the rows and columns flash randomly one at a time , why not sequentially ?? to increase the p3oo signal amplitude cause it is proportional wiz the expectancy of the flash..they are two speeds applied , fast ISI (“312.5 ms “inter stimulus interval) ans slow ISI (62.5ms), short ISI produce higher ITR (information transfer rate) while high ISI produced higher amplitudes , when a p3oo is generated the indicates an intended row or column , the second time its generated indicated an intended intersecting row or column the intersection of them is the targeted letter..
Subject four: Blinks And Speller integration
Using the p3oo speller and the blink detection technique i made the occurrence of a blink a selection of the current flashing row or column, and the the next blink accordingly , at last displaying the intended intersection cell .. we used the speller along with blinks in case we couldn’t apply p3oo for the keyboard usage…
The figure to the right is the P300 speller
Finally i now consider myself a Cap 2 Monitor official blog user, with a line underneath me
Menna Tawilla
Muhammad Omar
salehlotfi
Hani Amr
basmaelbanna
