There is quite wide agreement on the fact that the peak frequency of the EEG power spectrum (which generally corresponds with "alpha" oscillations) can vary slightly from subject to subject, and therefore the traditional definition of frequency bands (such as taking alpha from 8 to 12 Hz) could overlook more subtle differences between subjects. Moreover, lower and upper alpha band display distinct properties in some situations, which makes the distinction even more necessary (see for example several studies by Klimesch et al.)
This is particularly relevant when working with patients with Parkinson's (as is my case), who have on average a lower individual alpha frequency (some of them as low as 7 Hz!). However, in performing data analysis, I found out that it is not always so straight-forward to compute the individual peak: 1) because a good number of subjects do not show a clear peak, or they show more than one; 2) because the peak might differ slightly in different areas of the brain - and so what do I choose? 3) because the peak might differ in different conditions (during task performance vs resting state, or eyes open vs closed).
I would like to hear some opinions/comments from the community. Have you dealt with such issue, and how?
I am delighted to see this question raised. It highlights not only the potential significance of iAPF (individual Alpha Peak Frequency), but a fundamental problem with how "alpha" is measured. I think many leaders in the field would recommend that, rather than going by some pre-specified number, to determine what is actually "alpha" in any given subject, one should follow the original definition of alpha: the dominant frequency in the 8-13 Hz range in the subtraction of eyes open from eyes closed resting EEG (or words to that effect). As you point out, it is not actually quite that simple because iAPF may be different for different areas of the brain, and there may appear to be multiple alphas (or a bread peak), but you are in a good position to push this field forward by paying attention to these variables, and determining which are relevant for, for example, assessing drug effects, or predicting cognitive effects etc. There are many papers that mention the potential significance of iAPF, and two good recent ones are Arns et al., Brain Stimul. 2012 Oct;5(4):569-76, and Arns et al Appl Psychophysiol Biofeedback. 2012 Sep;37(3):171-80. (they relate to depression and ADHD not PD, but might be helpful). Good luck, and keep posting about this!
I have to agree with Eric Harris that this is really an important issue.Besides the different alpha band activities related to cognition, the EEG-alpha rhythm is one of the "hallmarks" of the resting brain. Not only the interindividual differences (i.e. differences of alpha peak frequency) have impact on analyis of EEG-data, but also do have the intraindividual changes that take place during the transition from wakefulness to more drowsy states. After closing the eyes, a dominant alpha rhythm over the occipital leads will apear. With increasing drowsiness during the resting state, a transition to more parietal and finally anterior brain areas will take place along with a decrease of the individual frequency peak. Therefore, when using resting state data, it is important what one wants to analyze: if you are interested in vigilance shifts, you should take into account all spatial and frequency distributions of the alpha band (see for example the VIGALL-algorithm of our research group; Hegerl et al. World J Biol Psychiatry. 2012). On the other side, if you want to process EEG data that is restricted to more anterior sites, you should look at the specific peak frequency at the frontal leads. Some interesting work on EEG band distribution and changes during rest have been done by e.g. De Gennaro (e.g. Clin Neurophysiol. 2001) or by Tsuno et al. (2002).
I also added a pic of a clearly distinct EEG alpha peak at frontal sites (9Hz) and posterior sites (10 Hz) during a 15 minute resting state EEG-recording.
This recent paper is relevant to the discussion: http://www.ncbi.nlm.nih.gov/pubmed/24508648
Read some paper of the literature on IAF (Individual Alpha Frequencies)
I must confess that I am not an expert in this field (merely a very interested party), but I will still suggest the following generalization: it doesn't matter so much what you measure or how you analyze it, as long as you are very clear in you description of what you analyzed, how you analyzed it, and why.
I wish I could provide a more specific, data-based answer, but it seems to me that the whole field of QEEG is growing/changing/refining a great deal, and I doubt that there is a consensus in the field about what to do, Therefore, I encourage you to think carefully about what specific question or feature of EEG interests you, design a good experiment, and see where that leads.
Good luck!
Eric
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