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I came across this research article "Case Study of Ecstatic Meditation: fMRI and EEG Evidence of Self-Stimulating a Reward System" about the brainwaves when a Yogi went into a meditative level.

I know that we have 5 types of brainwaves:

  • Gamma 40Hz~
  • Beta 14-40Hz
  • Alpha 7.5-14Hz
  • Theta 4-7.5Hz
  • Delta 0.5-4Hz

As for Meditative Stages, we have 8 stages:

Material

  • Jhana 1
  • Jhana 2
  • Jhana 3
  • Jhana 4

Immaterial

  • Jhana 5 / Arupa Jhana 1
  • Jhana 6 / Arupa Jhana 2
  • Jhana 7 / Arupa Jhana 3
  • Jhana 8 / Arupa Jhana 4

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As for what I've read on the paper, they could only start from Jhana 2 as "Jhana 1 was not practiced because the associated head movements would induce excessive artifact". Until Jhana 5 as there is some problem with Jhana 6-8 because the "fMRI recording then ended due to scanner memory limitations (421 volume maximum)".

How can I transform each meditative states [time-series data] into brainwaves frequencies?

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  • $\begingroup$ Try to specify a little more the question or the reasons for it, very interesting article and I hope to see more content about meditation on the site. $\endgroup$ – hexadecimal Jun 8 '17 at 15:23
  • $\begingroup$ Actually i don't know how to read the diagrams. I want to know what's the frequency for each jhana. Well maybe alpha1 for jhana 2 alpha2 for jhana 3 and theta for jhana 4 $\endgroup$ – LomX Jun 9 '17 at 14:04
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Thank you for your interesting question!

Generally, there are three types of methods to process your EEG time-series data:

  • Time domain methods (e.g., regression, statistical analysis on your EEG time-series data, etc.)

  • Frequency domain methods (e.g., Fourier transform)

  • Time-frequency domain methods (e.g., Short-Time Fourier Transform, Wavelet Transform, Gabor Transform, ) Wikipedia

You may use time-frequency methods to categorize or classify those states, if you will. It is very simple. Time-frequency methods transform your [1-D] time-series EEG data into a new [2-D] domain that you can see both time and frequency information.

[1-D] Frequency domain methods only return frequency information of your [1-D] EEG time signals, which means that your [1-D] time data will be lost.

[1-D] Time domain methods only return [1-D] time analysis of your EEG signals, which also cannot help you to capture the frequency information.

I suggest you to use Continuous Wavelet Transform or Discrete Wavelet Transform to do this job.

There are many tools/languages that might help you to do so: MatLab, Python, and such. If you might have a programmer around you, can probably help you in a few hours or a day to pass your [1-D] EEG time data through one of these [2-D] time-frequency methods and visualize the outputs.

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