# How to map EEG time-series and visualize meditative states frequencies?

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

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?

• 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. Commented Jun 8, 2017 at 15:23
• 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
– LomX
Commented Jun 9, 2017 at 14:04

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, etc.) 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 in exchange for having [1-D] frequency data.

[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.

May 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, s/he 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.