Monday, December 30, 2013

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

Sunday, December 22, 2013

Live Spectrograms in Processing!

For any regular readers of this blog, you know that I really like to visualize EEG data using spectrograms.  Unfortunately, when using OpenBCI, none of our software has a spectrogram display.  As a result, all my spectrograms are made after the tests are done.  While this is OK, it's not as good as having a real-time spectrogram.  So, I finally took the time to modify the OpenBCI GUI to include a live spectrogram.

As an example of how useful a spectrogram can be, look at the data below.  The picture is a screenshot from my newly-modified OpenBCI Processing GUI.  It shows EEG data recorded from the back of my head (O1-Fz).  My eyes were closed for the entire recording.  The screenshot was taken live (in the OpenBCI GUIs, press "m" to take a screenshot)...this is what the GUI was showing while the data was actually being recorded.

Screenshot from the OpenBCI "Simpler" Processing GUI.
Note the newly-added spectrogram plot.
I was recording Alpha waves from the back of my head (O1-Fz).

What does this screenshot show?  It shows alpha waves.  Specifically, because my eyes were closed and because the electrode was on the back of my head, it is showing my posterior dominant rhythm.  Based on the traditional spectrum plot on the left, it appears that my alpha waves are ~9 Hz in this recording.  Looks fine.

Now look at the spectrogram on the right.  it shows the same alpha wave signal, but now you can also see that the amplitude of my alpha increases and decreases through time (the color oscillates between red and light blue).  It is not a steady signal.  I find this very interesting.  The ability to see in both time and in frequency *at the same time* is the beauty of a spectrogram.

As another example, the screenshot below shows some Mu waves.  Here, I put an electrode on the left side of my head (C3) and tried to relax enough to make some Mu waves.  Mu waves are really difficult for me. I cannot get long sustained Mu waves like I can do for Alpha waves.  Instead, I can only seem to get 2-3 second long bits of Mu wave (as seen below).

A short segment of Mu waves recorded from the side of my head (C3-Fz)

With the regular spectrum plot on the left, it is hard to tell how long my Mu waves are sustained.  Now that I have the spectrogram, I can better see how long I'm sustaining my Mu waves, which means that I have better feedback for practicing making Mu waves.  In this way, you could say that it's a tool for neurofeedback.

Since I think that spectrograms are so useful, I've pushed this modified version of the "Simpler" Processing GUI up to the OpenBCI GitHub (link below).  Note that the spectrogram is only in the "Simpler" version of the Processing GUI.  The "Simpler" Processing GUI is designed to visualize just one or two channels of EEG data instead of all 8 channels that OpenBCI allows.

OpenBCI Processing GitHub:

Being limited to one or two channels means that this GUI might also be useful for OpenEEG (which is a two channel device).  All I have to do is alter the routine that interprets the data from the OpenBCI board so that it instead knows how to interpret the data format used by OpenEEG.  That should be pretty easy.  Look for a future update!

Tuesday, December 17, 2013

EEG While Meditating

Way back in September, I was with the OpenBCI folks at Maker Faire NYC showing off our very first OpenBCI prototype.  I was there wearing my bright blue EEG cap doing live demos of my brain waves.  It was fun.  On day 2, however, there was a guy who came up and demanded (politely) that he wear the EEG cap.  Given how long it takes to setup the cap correctly (with all the EEG gel and whatnot), I tried to decline his request.  But he persisted.  He told me that he was a meditator and that he needed to know if anything was actually happening in his brain when he was meditating.  Wow, you got me hooked me.  Let's do it!

Non-Meditating:  First, just so we all recognize what a normal, messy EEG recording looks like, check out the graphs below.  These are spectrograms of EEG signals recorded from my head using OpenBCI V1 and an EEG cap with tin electrodes.  The top plot is from the front of my head (Fp1-Fz) and the bottom plot is from the back-left area of my head (I think that its actually T5-Fz).  Note that both signals are highly corrupted by low-frequency artifacts from blinking my eyes.  Otherwise note that, when my eyes are closed, I show a nice signal ~10 Hz in the back of my head.  This is the classic posterior dominant rhythm that occurs in the Alpha band.  Notice that, besides the alpha and besides the eye blink, there is not much structure anywhere else in mostly just looks like messy noise.  Fine.

My EEG With My Eyes Open and Closed (No Meditating).
The arrows point to alpha waves that are commonly seen when one's eyes are closed.

Meditating:  So, now we turn to the data from my meditating friend.  The spectrograms below show data that we recorded from him using the same OpenBCI board and the same blue EEG cap.  In these plots, my friend was already meditating by the time I started to record the data.  So, to the left of the vertical line, this is data when his eyes were closed and he was meditating.  To the right of the line, he was not meditating and his eyes were open and closed at various times.  I find this data to be quite impressive.  Notice that there is clearly a band of signal energy between 15-20 Hz while he is meditating.  In the second half of the data, where his eyes are closed, there is maybe some of that 15-20 Hz signal, but it isn't nearly as strong as during the meditation.  I think that it is very cool that we appear to be seeing a physical (well, electrical) effect in his brain due to meditation.  Additionally, note also that he shows no alpha waves (nothing around 10 Hz) when his eyes are closed.   Is the meditation suppressing his posterior dominant rhythm, or does he just not exhibit one?  I don't know, but it's cool.

EEG recorded from a meditator.  Left of the line, he is meditating.  Right of the line, he is not.
Notice the signal energy between 15-20 Hz (and the absence of alpha waves) when he is meditating.

What Does It All Mean?  I make no claims regarding meditation being good or bad.  I make no claims regarding EEG signal energy in the 15-20 Hz band being good or bad.  All I know is that we can measure changes in his brain waves and that they appear to be due to him meditating.  That's really cool to me.

More Learning:  Via email, I showed this data to with my meditating friend.  He sent me back an outstanding link that was a survey of different schools of meditation and their relationship to changes in measured EEG. If you're into that kind of thing, it makes for fascinating reading:

Travis, F., & Shear, J. "Focused attention, open monitoring and automatic self-transcending: Categories to organize meditations from Vedic, Buddhist and Chinese traditions." Consciousness and Cognition (2010)

Have any of you recorded your brain waves while meditating?  What did you find?  Let me know!

Follow-Up: I recorded another meditator.  Check out his data here!

Thursday, December 12, 2013

OpenBCI on Kickstarter

My friends at OpenBCI have launched their Kickstarter!  If you are interested in a low-cost, hacker-friendly, EEG system, you might want to check it out.  I'm really excited for them.

Tuesday, December 3, 2013

Self-Noise of OpenBCI with More Data

After posting about my short-duration measurement of the self-noise of the OpenBCI board, there was a comment that discussed EEG applications where very low frequency signals were being used.  To understand the self-noise of the OpenBCI board at these low frequencies, I repeated my self-noise measurements, but used a much longer hour instead of ten seconds.  I also to the opportunity to measure the noise on five channels, not just one like before.  Here are my results.

Measuring Self-Noise of Five Channels of OpenBCI by
Jumpering Them All to Analog Ground
Setup:  The setup that I used was the same as before except that I jumpered the first five inputs to analog ground, instead of jumpering just the first channel.  Also, yesterday, I de-activated all of the channels except for the first one.  Since today's test used five channels, I kept the first five channels and only de-activated the last three.

Data Collected:  I recorded about an hour of data.  Near the end of the data, there was a spike that I could see in the graphs...I must of bumped the setup with my hand while I was doing other things.  So, I trimmed the data to remove the spike.  Overall, I was left with 3180 seconds of data.  The lowest possible frequency that could be represented in this data is about 1/3180 = 0.00031 Hz.  That is a very slow signal.

Results for One Channel:  After removing the DC offset (ie, the mean) of the entire 3180 second recording, and after lowpass filtering the data with a cutoff at 65 Hz, I get the histogram shown below.  This is for Channel 1.  As can be seen, it is a nice Gaussian shape, which is very smooth because of the huge number of data points in my 3180 second recording.  The standard deviation is 0.15 uV.  Since the standard deviation is the same as the RMS value (when the mean is removed), the RMS noise value for this channel for this recording was 0.15 uVrms.  This is very close to the 0.16 uVrms value that I recorded yesterday for the shorter 10 second recording.  This agreement makes me feel very good.

Histogram of 3180 Seconds of Noise Recorded from
OpenBCI with its Inputs Jumpered to Analog Ground.

Noise Spectrum:  To see how the noise level varies with frequency, we can plot the spectrum of the signal.  Since the recording is so long, the spectrum reaches down to very low frequencies.  For channel 1, I got the spectrum shown below.  As can be seen, it has two regimes: (A) a flat "white noise" region above 0.07 Hz and (B) a sloped "1/f Noise" region where the noise density increases as the frequency gets lower and lower.  This "1/f" behavior (the "f" is for "frequency") is very commonly seen when evaluating the low-frequency noise of analog amplifiers.  It is impressive that the 1/f noise doesn't begin until about 0.05 Hz.  As a result, even at a frequency of 0.001 Hz, the noise level is only about 0.3 uV/sqrt(Hz).  That seems pretty good to me.

Spectrum of Self-Noise Recorded from OpenBCI
with its Inputs Jumpered to Analog Ground.

60 Hz Noise:  What is a bit surprising in this spectrum is the sudden appearance of the 60 Hz noise (there was none seen in my data yesterday) and of a spike at 0.080 Hz.  I'm thinking that, because I now have 5 channels active instead of one, and because the 5 channels share the single SRB2 input as the reference for the differential amplifier, that the common-mode rejection capability of the differential amplifier is degraded because of the 5x leakage current through the common SRB2 components.  Any imbalance on the two legs of a differential input will degrade the common-mode rejection, and the datasheet for the ADS1299 warns of this behavior.  It looks like we're seeing it.  While it is unfortunate, the level of the 60 hz (and 0.080 Hz) is still quite reads about 0.3 uV/sqrt(Hz).  Since the "sqrt(Hz)" part is confusing for sinewave-like signals, I zoomed in on the graph around 60 Hz and I saw that the bandwidth of the 60 Hz signal is about 0.01 Hz.  From this, I estimate that the RMS value of the 60 Hz signal is 0.3 uV/sqrt(Hz) * sqrt(0.01Hz) = 0.03 uVrms.  So, it is a very small signal and probably not much to be concerned about.

Comparison Across Five Channels:  Unlike yesterday, where I measured just one EEG channel, today I measured five.  I would have done all eight, but I didn't have enough jumper wires.  For the five channels of data, I made the same plots.  They are very similar to each other.  The white noise is flat and the 1/f noise is similarly sloped.  They all contain the 0.080 Hz and the 60 Hz spikes.  The only real difference is that the amplitude of the two spikes varies a bit from channel to channel.  That does not change my overall conclusion that the five channels are sufficiently similar.  For the 3180 sec recording, I've tabulated the RMS noise (up to 65 Hz) below:

  • Chan 1: Noise = 0.15 uVrms
  • Chan 2: Noise = 0.16 uVrms
  • Chan 3: Noise = 0.17 uVrms
  • Chan 4: Noise = 0.18 uVrms
  • Chan 5: Noise = 0.15 uVrms

Conclusion:  This longer, multi-channel recording that I performed today confirms the noise levels that I recorded yesterday.  Today's longer recording is also able to reveal the low-frequency noise behavior of the system, which transitions from white noise to 1/f Noise around 0.07 Hz.  At 0.001 Hz, the noise density is about 0.03 uV/sqrt(Hz).

Other EEG Systems:  While I think that these noise levels are pretty low, I still do not have any comparison data from other systems.  Does anyone know how they perform?

Monday, December 2, 2013

Self-Noise of OpenBCI

An important quality of any EEG system is its noise floor.  If an EEG system has a noise floor that is too high, the noise might mask the small EEG signals that you're trying to measure.  So, when working with an EEG system, it is important know that its noise floor is low enough.  I've been working with the OpenBCI system, but I don't know what it has for a self-noise level.  So, I decided to measure it.

Measuring the Self-Noise of the OpenBCI Board by
Shorting the Inputs to Analog Ground

Expected Noise Level:  The heart of the OpenBCI board is the Texas Instruments ADS1299, which is a "low-noise, 8-channel, 24-bit analog front-end for biopotential measurements". Its datasheet says that it has a self-noise of 1 uV over a bandwidth of 0.01 Hz to 70 Hz.  That 0.01 Hz value is a very low frequency and would require at least 100 seconds of data, if not several hundred seconds of data (to get a good average) to properly evaluate.  Since I don't care about signals down to 0.01 Hz, and since I want a faster test, I looked at the datasheet for other statements regarding its noise level.  The best value that I found was in the Table 4 in the data sheet, which I copied below for convenience.  A footnote on the table says the values are based on a recording of 1000 data points.  When running at 250 Hz, this means that their sample had 4 seconds of data and not the 100s of seconds of data needed to recreate the value reported for 0.01 Hz.  This is a shorter test.  Great.

Table 4 from the ADS1299 Data Sheet Showing Self-Noise for a Gain of 24.
For a sample rate of 250 Hz, the noise level is 0.14 uVrms for a 65 Hz bandwidth.
We run the ADS1299 at a sample rate of 250 Hz, so looking near the bottom of the table, we see that we should expect a noise level of 0.14 uVrms for a bandwidth of 65 Hz (presumably 0.25 Hz up to about 65 Hz).  To make sure that I'm interpretting this table correctly, I found Figure 3 in the datasheet (copied below), which shows a sample of noise recorded with a gain of 24 and a sample rate of 250 Hz.  Based on the amplitude of the noisy signal, I would say that this graph is consistent with the 0.14 uVrms value that we took from the table above.  That's good.  Now, let's do our own experiment to see if the self-noise of the OpenBCI board is at this low level.

Figure 3 from the ADS1299 Data Sheet Showing a Sample of Self Noise.
This picture is consistent with the 0.14 uVrms level given in the table.

How to Measure "Self Noise":  When evaluating the self-noise of a sensing device, what you want to measure is the amplitude of the signal that the device thinks that it sees, even when there is not "real" signal present.  If there is no real signal present, then anything that is present is noise.  One easy way to ensure that no real signal is present is to simply short the inputs to ground.  That's what we'll do.

Shorting the Input to Ground:  For the OpenBCI board, each input is actually a differential measurement between the input and the common reference labeled "SRB2".  So, as you can see in the picture above, I shorted "input 1" to "SRB2" with the yellow jumper wire, and then I shorted SRB2 to analog ground with the white jumper wire.

Other Hardware:  The OpenBCI board was mounted on an Arduino Uno.  It was plugged into a PC via USB.  The Arduino (and, therefore, the OpenBCI board) were being powered from USB.

Software Setup:  I configured the OpenBCI board for normal EEG data collection.  This means that it was using a sample rate of 250 Hz with a gain of 24.  All channels except for channel 1 were turned off.

Results:  I recorded 10 seconds of the digital values that were output by OpenBCI board via the Arduino.  After scaling the raw counts into volts (1 count = 0.022 uV), and after filtering the noise to a bandwidth of 0.1-65 Hz, I made the time-domain plot below, which mimics the plot above from the ADS1299 datasheet.  As can be seen, it looks very similar.  The RMS value is 0.16 uV, which is very close to the 0.14 uV reported in the data sheet.  Given that datasheet values are usually very hard to achieve in practice, I find the 0.16 uVrms value to be a remarkable result.

Noise Recorded from OpenBCI with its
Inputs Jumpered to Analog Ground.

Characteristics of the Noise:  In addition to the RMS value, it can also interesting to look at the histogram and spectral properties of the noise.  The histogram below is for the same sample of OpenBCI data shown above.  It appears to be Gaussian shaped, which is good because that is what is expected.  The spectrum shown below  is also good because the spectrum is generally flat all the way up to the Nyquist frequency for this sample rate (Nyquist = half the sample rate = 250 Hz / 2 = 125 Hz).  Looking in detail, the magnitude of the noise density appears to average about 0.02 uV/sqrt(Hz) for frequencies up to 65 Hz, which is also good because it is consistent with our RMS value being 0.16 uV (this is consistent because 0.16 uV / sqrt(65 Hz)  = 0.02 uV/sqrt(Hz)).  Good news all-around!

Histogram of 10 Seconds of Noise Recorded from OpenBCI with its
Inputs Jumpered to Analog Ground.
Spectrum of Self-Noise Recorded from OpenBCI with its
Inputs Jumpered to Analog Ground.
Conclusion:  Based on this single 10 second recording, the self-noise of channel 1 of my OpenBCI board is 0.16 uVrms over the bandwidth of 0.1 to 65 Hz.  This is consistent with the value reported in the datasheet for the ADS1299 (which was 0.14 uVrms), so I have confidence in the value that I measured.

Other EEG Systems:  This 0.16 uVrms value sounds pretty good to me, but I do not have much experience with other EEG systems.  How about you?  Do you know the noise level for other systems?  Can you share the values?  I'd love to build a table comparing the different systems!

Follow-UP:  Based on reader's comments, I've extended my measurements to go lower in frequency and to look across multiple channels.  Check out the results here!