Fast Fouier VS. Autoregressive

http://docs.google.com/Doc?id=dhrddpn6_54c69rwfc3

Fast Fourier

I gave two talks here is the first:


Fast Fourier Transforms
in less than 15 min!

What is 'Fast Fourier Transforms?

Fast Fourier Transform algorithms are used to compute the Discrete Fourier Transform and its inverse.

Ok, so what is 'Discrete Fourier Transform?

Discrete Fourier Transforms are used to transform one function into another that is called the Frequency domain. This makes sense because in Digital Signal processing we don't care nearly as much about the numbers as we do the FREQUENCY they are observed! Plus, the frequency is usually a less complicated number then the original input.

Still with me? Lets talk more about the Frequency Domain!

We can think of the Fourier Transform as converting the signal information to a magnitude and phase component of each frequency. Now instead of the signal input potentially being all over the place (think eye blinks or other artifacts in EEG data) the current signal in influenced by all of the previous signals and therefore cannot leave the bound of the Frequency Domain!

(Drawing time!)

Oh no, close your eyes, now its time for the math...


So this is a basic Discrete Fourier Transform. The most notable aspect is e^ -(2πi/n), this is like a log curve so every point in the signal with be plotted along this curve.

(More Drawing time!)

That wasn't so bad lets close with a list of advantages and disadvantages:

Advantages:

• Fast! Or at least faster than trying to process the un-transformed signal

Disadvantages:

• Loss of precision due to floating point numbers
• Signal segments are discrete and calculated independently. In this aspect this signal isn't very efficient

The next chapter in this exciting series:
Fast Fourier vs Autoregressive analysis

for pattern rec

I can look at "Inter-trial coherence " to figure out brain oscillations (state, waves)

Now I need to figure out response time for every event , what the different events are, and

adaboost papers

Adaboost papers:

found:



need to find:

1. Adaboost for improving classification of left and right hand motor imagery tasks
• http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1499830
• ordered on ILL
  
2. A new approach in the BCI research based on fractal dimension as feature and Adaboost as classifier
• http://www.iop.org/EJ/abstract/1741-2552/1/4/004
• ordered on ILL
3. Medical and Biological Engineering and Computing: A comparison approach toward finding the best feature and classifier in cue-based BCI
• http://www.springerlink.com/content/e436280xh0u67405/
• ordered on ILL
  

Exploring EEG Lab

baseline latency range

1. split signal into EPOCHS
2. saved EPOCH file to TXT
1. -.003 before event .003 after but I don't know what EVENT
2. IF I can find out EVENT with corresponding EPOCH then ready to split into patterns and targets
3. I need to look into source coherence or just coherence.
1. This should help me figure out if alpha, beta, gamma
4. With (1,2) & (3) figured out I can solve Dr. Keith's problem
   
1. Dr. Keith's problem solved I will be able to:
   
1. Classify ERPs in my own data when I run subjects
2. Monitor my own subject's 'Attention' vs 'distraction'
1. Gamma vs Alpha waves

For tomorrow:

1. Make a schedule
2. look into coherence in EEGLAB & in General lit
3. I gave a presentation on Fast Fourier Transforms last week I need to prepare Auto Regressive VS FFTs presentation for next wednesday