Breathing Meditation – Alpha Amplitude

It turns out that my previous post on EEG and meditation was surprisingly popular. The post even got one of my friends interested enough that he, too, wanted to see what happened to his EEG signals while he was meditating. So, we hooked him up to one of my OpenBCI boards and took some measurements! Here’s the story of what we found together.

My second willing meditator.
(And the blue cap returns!)

Goal: My goal with these recordings is simply to see if meditation has a measurable effect (any effect) on one’s EEG signals. I’m trying understand if a particular form of mental activity (ie, meditation) can be measured objectively.

Setup: For this set of recordings, we decided to go the Full Monty and bring out the blue EEG cap (see photo above). This was the same cap as used with the previous meditator. We got the cap as part of a kit that we bought from Biopac. Our cap has lots of electrodes. We chose to use the eight electrode locations shown below. Our reference electrode was towards the front of the head along the centerline (near FPz/AFz) and our driven ground (aka “bias”) was attached behind the right ear (right mastoid). We used the electrode gel that came with the kit from Biopac. For data logging software, we used the OpenBCI GUI that was written in Processing.

Electrode locations used for these recordings.
Fp1, Fp2, C3, C4, P7, P8, O1, O2.
Reference electrode was near Fpz.
Driven ground (“bias”) was the right mastoid.

Two Test Scenarios: My meditating friend performed two sets of recordings: one while meditating and one while simply relaxing. The non-meditating data will act as a baseline against which we compare the meditating data. Note that the two recordings were done in back-to-back sessions without removing the electrode cap.

Test Procedure: Both session started with an initial period with his eyes opened followed a long period with his eyes closed. It is during this eyes-closed period where he was either meditating, or he was simply relaxing but not meditating. When he is meditating, my friend’s meditation style is breathing meditation, where he focuses solely on his breathing and on his body’s response to his breathing.

Example Results: Example data from his baseline recording session is shown in the spectrogram below. This data is from an electrode on the back of his head (channel 7, which is at O1). In this figure, you can see that once he closes his eyes, he exhibits a strong EEG signal around 10-12 Hz, which is in the Alpha band. This eyes-closed Alpha rhythm is a very typical EEG pattern. In this recording, there is also a faint signal between 20-25 Hz, which is simply a harmonic of the fundamental 10-12 Hz Alpha wave. Overall, this Alpha-dominated signal seems to be very consistent with most other eyes-closed data that we’ve recorded from other individuals (including myself).

Example EEG data recorded during the baseline (ie, not meditating) session. This is from the back of his head. The horizontal stripe of signal energy is around 10-12 Hz, which is in the Alpha band. Alpha waves are indeed commonly seen when one’s eyes are closed.

Full Baseline Results: The plot above shows data from just from one location on the head. The figure below, by contrast, shows all eight channels of EEG data that we recorded. It gives a fuller picture of what is happening during the baseline (non-meditating) recording session. Like in the single example above, the plot below shows that many of the electrodes pick up the steady Alpha rhythm when he closes his eyes. You can see, though, that the Alpha rhythm is much stronger in the back of the head than in the front. Since this eyes-closed “posterior dominant rhythm” originates in the now-idled visual cortex (which is the back of the brain), the fact that the Alpha waves are strongest in the back of the head and weakest in the front is exactly what we would expect to see.

EEG data recorded while relaxing but not meditating. Notice that the Alpha waves (the horizontal stripe of energy in each plot) are strongest towards the back of the head. Click on the figure to enlarge.

EEG Data While Meditating: Now we get to the good stuff. Now we turn our attention to the data recorded while my friend was meditating. The figure below shows the data recorded while he was meditating. The meditating began when he closed his eyes, so I’ve limited my examination just to the eyes-closed data. Clearly, the dominant feature is that horizontal stripe of energy in the 10-12 Hz band representing the eyes-closed Alpha rhythm. This is the same kind of signal that we saw when he was not meditating. So, to first glance, meditating does not have an obvious effect on his brain waves. For example, it did not make the Alpha waves disappear nor did it make any new signals appear. If there are any changes due to meditating, the changes must be subtle.

EEG Data recorded while meditating by focusing on his breathing. Alpha waves still dominate. Click on the figure to enlarge.

Change in Alpha Amplitude: Comparing these two figures more closely, one change that I do see is that the intensity of the Alpha waves appears to decrease when he is meditating. Because this change in amplitude is difficult to see quantitatively in the spectrograms, I replotted the data as basic spectrum plots, as shown below. In these new plots, I’ve included just the eyes-closed data. These new plots clearly show that the dominant EEG energy is between 10-13 Hz, which are the Alpha waves. We see that the Alpha waves in both the baseline and meditating recordings are centered around 11.72 Hz, so meditating did not change the speed of his Alpha waves. We do see, however, that the amplitude of these Alpha waves are smaller when meditating. In fact, we see that the amplitude is cut nearly in half (6.1 uVrms down to 3.6 uVrms). That’s a pretty big change! While we cannot yet be sure that change was caused by the meditation (repeated tests would be necessary to confirm a cause and effect relationship), this data is highly intriguing and begs for additional recordings. This is cool.

Amplitude of the EEG signals recorded when his eyes were closed during the baseline test (left figure) and during the meditation test (right figure). As can be seen, the strongest signals are between 10-13 Hz, which are Alpha waves. His Alpha are centered on 11.72 Hz. You can see that the amplitude of his Alpha decreases while he is meditating.

Comparison of Alpha to the Meditator at Maker Faire: Looking at my previous post for the meditator at Maker Faire, we saw that the previous mediator had very different brain patterns than seen above. First, the meditator at Maker Faire showed no Alpha waves at all. None. While most people do exhibit Alpha when the eyes are closed, eyes-closed Alpha is not universal. So, it is possible that the meditator at Maker Faire is simply one of those individuals who does not exhibit eyes-closed Alpha. Or, as an alternate conjecture, perhaps the act of meditation suppresses Alpha waves. Perhaps our highly-experienced meditator at Maker Faire completely suppressed his Alpha response, whereas the novice meditator shown above only showed moderate suppression of his Alpha response. Again, we have insufficient data to make any real conclusions, but this is very intriguing.

Comparison of Beta Waves to Meditator at Maker Faire: Another key finding from the meditator at Maker Faire was that his meditation seemed to generate EEG activity in the 15-20 Hz band, which are the low-end Beta frequency range. His generation of Beta waves is in contrast to the novice meditator shown here, who showed no change in Beta activity. Perhaps the lack of Beta activity is due to his inexperience, or perhaps it is due to a difference in the type of meditation. As discussed in the Travis paper linked previosuly, different types of meditation are known to correlate with different EEG responses just as different types of mental activity can generate activity in different EEG frequency bands. So, perhaps the Maker Faire meditator was performing a “focused attention” style of meditation (which is associated with increased Beta) whereas today’s meditator was more of an “open awareness” style of meditation (which is not associated with Beta). I am not properly educated in the different styles of meditation, so I really should not comment on this further. Perhaps it would be best to get the individual meditators themselves to describe their own meditation style relative to the criteria defined in the Travis paper. That would probably be the best approach.

Conclusions: With only a single pair of recordings from a single individual from a single sitting, we cannot draw any solid conclusions. What we can say is that we happened to see a decrease in the amplitude of the alpha waves in the back of the head during meditation. If this change is actually due to the meditation, it shows that the meditation is indeed having measurable changes on brain activity. I have no idea whether changing the amplitude of the Alpha rhythm is a good or bad thing…I just think that it is interesting that we can measure any change at all. I would love to be able to confirm this finding or to see it in other people.

Next Steps: This has been a very basic analysis of the EEG data that we recorded. For example, in quantifying the amplitude of the Alpha waves, I simply looked at each EEG channel in isolation from the others. Sure, I noted that the Alpha were strongest in the back, but I did not look at any more subtle changes with how the different channels correlate with each other. It is possible that the act of meditation brings different regions of the brain into concert with each other. Or, maybe meditation does the opposite and causes different regions of the brain to become decoupled from each other. Either way, some sort of quantitative analysis of the correlation between the different EEG channels might expose additional changes in brain patterns due to meditation. I would find this kind of change to be very interesting. I don’t know what it would mean, but I would find it interesting. So, I guess that I’m saying that I am not yet done with this particular set of EEG data. I will pursue some kind of cross-channel analysis in my next post.

Until then, thanks for reading!

Follow-Up: Here is the analysis of the cross-channel coherence. Cool!
Follow-Up: The raw data is available as part of the OpenBCI repository on GitHub