I want to analyse my data using the individual alpha frequency to determine the theta and alpha frequencies, but I am not so sure about how I can do that. How could I do that using EEGLAB/MATLAB? Is that possible?
Dear Nastassja,
You can calculate the individual alpha frequency using the Neurophysiological Biomarker Toolbox (tightly integrated with EEGLAB).
You need to check the nbt_doPeakFit function, which will calculate the IAF and many other spectral markers.
See
http://www.nbtwiki.net/doku.php?id=tutorial:list_of_biomarkers#.UnunU42-OCo
and http://www.nbtwiki.net/doku.php?id=tutorial:compute_biomarkers#.UnunK42-OCo
Best wishes,
Simon-Shlomo Poil
The individual alpha frequency (IAF) peak can be defined as the frequency associated to the strongest EEG power within the extended alpha range (Klimesch, 1999).
In our paper about EEG rhythm sources in patients affected by Alzheimer and Frontotemporal dementia (Caso et al. 2012), we calculated the IAF as follows:
- Spectral estimation for each EEG channel, using an FFT based method
- Global power spectrum calculation, as average of all individual channel spectra
- Selection of the IAF as the frequency showing a power peak within the extended alpha range (7-13 Hz)
As suggested in a recent paper (Babiloni et al 2012), referencing to the IAF, you can calculate the edges of bands of your interest, i.e. delta (IAF-8 to IAF-6 Hz), theta (IAF-6 to IAF-4 Hz), alpha 1 (IAF-4 to IAF-2 Hz), alpha 2 (IAF-2 to IAF Hz), and alpha 3 (IAF to IAF+2 Hz). For example, if power peak in the extended alpha range was observed at 10 Hz (IAF), the frequency bands of interest were as follows: 2–4 Hz (delta), 4–6 Hz (theta), 6–8 Hz (alpha 1), 8–10 Hz (alpha 2), 10–12 Hz (alpha 3).
Babiloni C, Stella G, Buffo P, Vecchio F, Onorati P, Muratori C, Miano S, Gheller F, Antonaci L, Albertini G, Rossini PM. Cortical sources of resting state EEG rhythms are abnormal in dyslexic children. Clin Neurophysiol. 2012 Dec;123(12):2384-91.
Caso F, Cursi M, Magnani G, Fanelli G, Falautano M, Comi G, Leocani L, Minicucci F. Quantitative EEG and LORETA: valuable tools in discerning FTD from AD? Neurobiol Aging. 2012 Oct;33(10):2343-56
Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Brain Res Rev. 1999 Apr;29(2-3):169-95. Review.
Dear Nastassja, about alpha frequency, I totally agree with the answer the dr Lo Bour from Amsterdam gave to you. I would like to add some technical detail. I would advise you to use parametrical spectral estimation (via AR-AutoRegressive methods, i.e. either by Yule-Walker block algorithm or by recursive Levinson-Durbin one --> See Marple "Modern perspectives of spectral estimation", a non-recent technical excellent book) and then evaluate the total power within IAF +/- 1 Hz. An analogous parametric AR method was also applied by Prof. S. Cerutti - dept Bioengineering - Polytechnic of Milan, Italy, some decades ago, in order to get a HRV (Heart Rate Variability) spectra estimate from ECG. MATLAB is a perfect tool to implement the relative algorithm.
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