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Design guide

Biomedical signal analysis

Electro-encephalo-graphy (EEG) signal analysis through DSP algorithms

The recording of electrical signals emanated from human brain, which can be collected from the scalp of the head is called Electroencephalography (EEG). These signal's parameters and patterns indicate the health of the brain. EEG is the key area of biomedical data analysis.

Using Digital Signal Processing functions EEG signals can be analyzed to properly diagnose the patient. The latest biomedical embedded electronics systems with DSP processor can display the computed results helping doctor to save time in anayzing complex EEG waveforms.

The various DSP based analytic methods used to evaluate are,

1. Spectral estimation
2. Periodogram
3. Maximum entropy method
4. AR method
5. Moving average methods
6. ARMA method
7. Maximum likelyhoodmethod

Here we provide some basics of each methods and further links to study in more depth.

Spectral estimation: Spectral estimation helps in finding the pulse rhythms present in the EEG signal. The short segment of EEG data is analyzed for spectral parameters such as location and amount of spectral energy. Waveshaping filters are extensively used in this technique. Wave shaping filters produce desired output signal for given input signal.
If the desired signal is a unit impulse it's called spiking filters. Spiking filters can be used to determine the position of the locations of the concentrated energy signal.
Such study of energy concentration in EEG signal is called spectral estimation analysis

To know further read these papers/online materials

http://eprints.iisc.ernet.in/6870/1/Spectral_estimation_of_short_segments.pdf
http://www.commsp.ee.ic.ac.uk/~mandic/SE_ASP_LN/SE_ASP_Lecture_3_Modern_SE.pdf
http://outserv.cactus.org/~benjamin/X/marple.pdf
http://www.commsp.ee.ic.ac.uk/~mandic/SE_ASP_LN/SE_ASP_Lecture_3_Modern_SE.pdf
http://dspace.mit.edu/bitstream/handle/1721.1/4250/RLE-TR-493-15597448.pdf?sequence=1
https://darchive.mblwhoilibrary.org/bitstream/1912/2415/1/Briggs_thesis.pdf


Periodogram: Periodogram is nothing but the estimation of spectral density. This can be obtained through estimated correlation function.

Read these power point presentation at the link to know about periodograms used to analyze EEG signals,
https://noppa.tkk.fi/noppa/kurssi/t-61.5080/luennot/slides.pdf
http://www.maths.bris.ac.uk/~mazlc/TSA/case02.pdf


Maximum entropy method: Helps to measure the randomness and uncertainty associated with the EEG signal. Maximum Entrophy method works even if any information or constraints on a process X(n) are absent.

To know further on Maximum Entropy Method visit,,
http://www.commsp.ee.ic.ac.uk/~mandic/SE_ASP_LN/SE_ASP_Lecture_3_Modern_SE.pdf


AR method: Autoregressive (AR) is preferred when signal's frequency has sharp peaks. AR is popular because, the accurate estimation of PSD can be obtained by solving linear equations. AR model is called all pole method where, each sample of the signal can be expressed as a combination of previous samples and an error signal.

Read this below online source to learn more on AR Method:
http://www.rgi.tut.fi/courses/materiaali/71413/71413Lecture5.pdf


ARMA (autoregressive moving average) method: This model is suggested for modeling signals with sharp peaks and valleys in their frequency content and also signals with severe background noise.

The detailed explanation of ARMA model is available at,

http://www.rgi.tut.fi/courses/materiaali/71413/71413Lecture5.pdf

Maximum likelihood method: This blend the information already available based on prior knowledge and latest measurements. The value is optimal estimation of the actual value.

For more details on this method, read the book available on books.google.co.in
The book name is EEG signal processing By Saeid Sanei, Jonathon A. Chambers
To access this book visit books.google.co.in and enter the book name and author


The other popular online reference material on biomedical signal processing is,
Biomedical Signal Processing: Principles and Techniques By Reddy
To access this book visit books.google.co.in and enter the book name and author


 

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