<p>Thank you for your interesting question!</p>

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

<ul>
<li><p>Time domain methods (e.g., regression, statistical analysis on your EEG time-series data, etc.)</p></li>
<li><p>Frequency domain methods (e.g., Fourier Transform)</p></li>
<li><p>Time-frequency domain methods (e.g., Short-Time Fourier Transform, Wavelet Transform, Gabor Transform, etc.) <a href="https://en.wikipedia.org/wiki/Time%E2%80%93frequency_analysis" rel="nofollow noreferrer">Wikipedia</a></p></li>
</ul>

<p>You may use <code>time-frequency methods</code> 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. </p>

<p>[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. </p>

<p>[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. </p>

<p>May I suggest you to use <code>Continuous Wavelet Transform</code> or <code>Discrete Wavelet Transform</code> to do this job?</p>

<p>There are many tools/languages that might help you to do so: <code>MatLab</code>, <code>Python</code>, 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. </p>

CC BY-SA 4.0
2019-02-21T01:44:42.350Z
2019-02-22T05:49:34.690Z
2019-02-22T05:49:34.690Z
Emma
<p>I came across this research article "<a href="https://www.hindawi.com/journals/np/2013/653572/" rel="nofollow noreferrer">Case Study of Ecstatic Meditation: fMRI and EEG Evidence of Self-Stimulating a Reward System</a>" about the brainwaves when a Yogi went into a meditative level.</p>

<p>I know that we have 5 types of brainwaves:</p>

<ul>
<li>Gamma 40Hz~</li>
<li>Beta 14-40Hz</li>
<li>Alpha 7.5-14Hz</li>
<li>Theta 4-7.5Hz</li>
<li>Delta 0.5-4Hz</li>
</ul>

<p>As for Meditative Stages, we have 8 stages:</p>

<p><strong>Material</strong></p>

<ul>
<li>Jhana 1</li>
<li>Jhana 2</li>
<li>Jhana 3</li>
<li>Jhana 4</li>
</ul>

<p><strong>Immaterial</strong></p>

<ul>
<li>Jhana 5 / Arupa Jhana 1</li>
<li>Jhana 6 / Arupa Jhana 2</li>
<li>Jhana 7 / Arupa Jhana 3</li>
<li>Jhana 8 / Arupa Jhana 4</li>
</ul>

<p><img src="https://i.sstatic.net/TMNDD.jpg" alt="enter image description here"></p>

<p><img src="https://i.sstatic.net/3SJ2I.jpg" alt="enter image description here"></p>

<p><img src="https://i.sstatic.net/U7yfO.png" alt="enter image description here"></p>

<p><img src="https://i.sstatic.net/7hujs.png" alt="enter image description here"></p>

<p>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)".</p>

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

CC BY-SA 3.0
2017-06-08T15:23:20.900Z
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.
hexadecimal
CC BY-SA 3.0
2017-06-09T14:04:47.393Z
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
<eeg><meditation><signal-processing>
How to map EEG time-series and visualize meditative states frequencies?
LomX
# How to map EEG time-series and visualize meditative states frequencies?

(Asked by: username_0 on 2017-05-22)

I came across this research article "[Case Study of Ecstatic Meditation: fMRI and EEG Evidence of Self-Stimulating a Reward System](https://www.hindawi.com/journals/np/2013/653572/)" 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

![enter image description here](https://i.sstatic.net/TMNDD.jpg)

![enter image description here](https://i.sstatic.net/3SJ2I.jpg)

![enter image description here](https://i.sstatic.net/U7yfO.png)

![enter image description here](https://i.sstatic.net/7hujs.png)

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?

**Tags:** eeg, meditation, signal-processing

> **Comments:**
> **username_1**: 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.  
> **username_0**: 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  

---

## 1 answers

### Answer by username_2 on 2019-02-21. Score: 1

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](https://en.wikipedia.org/wiki/Time%E2%80%93frequency_analysis)

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.


---

