|Electrodes on my face to measure my EOG due to eye motion.|
As you can see in the picture above, I used three electrodes: one above my eye, one below my eye, and one to the side of my eye. I have no experience recording EOG, so I don't know what standard practice is. This is just what I happened to try.
In my setup, the primary electrodes were the ones above and below my eye. The electrode on the side was used only as my driven ground (or "bias" if that is your preferred language). It turns out that this connection wasn't really necessary (EOG signals are very strong), so you might be able to omit it.
The electrodes themselves are reusable gold-plated electrodes commonly used in EEG. Mine are similar to these. I stuck them to my skin using standard Ten20 EEG paste.
I plugged these electrodes into my OpenBCI electronics using my new, homemade adapter cable. My OpenBCI board was mounted to an Arduino Uno, which was pumping the data to my PC for logging and post-test analysis.
Once I turned everything on and started collecting data, I found some really interesting things. Again, I have no experience doing this, so I had no idea what to expect. Below is a graph of the voltage recorded during a period of time where I was looking forward, I then turned my eyes to look upward (keeping my head itself fixed), and then returned my eyes to looking forward. As you can see, the voltage clearly tracks the motion of my eye. When my eye was "up", the voltage was up. While that seems so sensible, it was not what I expected at all.
|Voltage recorded between an electrode above my eye and an electrode below my eye.|
What was I expecting? Well, I do have some experience seeing muscle artfiact in EEG signals due to clenching my jaw. Muscle artifact shows itself as a hashy, noisy, high-frequency signal. Since it takes muscles to move your eye, I assumed that "eye artifact" would also be hashy and noisy. I was wrong. It is a low frequency signal that reflects the actual position of the eye...not the changing position of the eye. Even a quick jump over to read about EOG on Wikipedia would have told me that.
To paraphrase Wiki, EOG measures the local effect of the electrical potential that always exists between the front and back of your retina. So, changing the orientation of your eye changes the how much each electrode "sees" the positive side of the retina versus how much it sees the negative side of the retina. Cool!
Zooming out on my data, let's look at a longer time span with more eye motion. The plot below shows the EOG data recorded for a variety of eye positions...looking forward, looking up, and looking down. As you can see, there is a clear relationship between the measured voltage and the eye position. Clearly, this system could be used as an eye tracker to tell (roughly) where your eyes are looking. I find this very exciting.
|EOG data recorded for multiple eye positions.|
The signals shown above were plotted after filtering. Whenever I present EEG/ECG/EOG data, I filter it to get rid of very high frequency noise and to get rid of drifting of the DC component of the signal. For EEG, I often filter away the signal energy that is below 0.5 Hz. Since I do not care about EEG signals this low, removing these low-frequency components makes the plots much easier to follow. But, if you use these filter settings for EOG, you get a completely different type of EOG plot than the nice ones shown above.
My previous EOG plots were bandpass filtered to only include energy between 0.02 Hz and 50 Hz. Below is a plot of the same EOG data, but using my typical EEG passband (0.5 Hz to 50 Hz). Notice that the plot looks very different.
|Same EOG data but filtered to remove low-frequency energy below 0.5 Hz.|
To be truthful, the graph above is what I first saw when looking at my EOG data. This is the graph I saw because I was viewing the data using my usual EEG filters (0.5-50 Hz). As a result, I thought EOG was only good for measuring eye motion. It wasn't until I started exploring the data in greater detail (changing one's normal filter settings is a great way to explore the data) that I discovered the plots that I show at the top. When I lowered my low-frequency cutoff to 0.02 Hz and got those plots, that's when I discovered that EOG is really about measuring the change in DC potentials due to your eye position. What a fun discovery!
So, that's my story of recording EOG with my EEG setup. It was really fun. My next step is to put electrodes to the sides of my eyes so measure side-to-side motion in addition to up-and-down motion. Also, I might try my homemade electrodes to see how well they do.
The real challenge with EOG is to keep the electrodes stuck on your face as you move your head around. One thing that I did try was to use my self-adhesive ECG electrodes. These stuck great! But, they sure did look silly...
|ECG electrodes stick better, but look much more silly.|
Follow-Up: Want to get the data used in this post? Try my github!
Pro tip: wrap the electrodes in a headband and monitor the signal during sleep, then trigger audio tracks when repetitive movements above 500 or below -500 are detected.
Are you think that, when the big eye excursions occur (the +/-500 mV trigger levels), that I'm in a sleep state that is susceptible to outside suggestion? Would the audio tracks affect my dreaming experience?Delete
Thanks for reading my blog!
Yes, I am think that; the big eye excursions are the same when you look around inside the dream world. It will flatline at around zero all night except when you are in the dream state.Delete
The audio tracks will bleed into the dream world every blue moon, especially when background music is playing, and can then be acted upon.
This comment has been removed by a blog administrator.ReplyDelete
um curious about ur articleReplyDelete
Can i have ur EOG sample signal and matlab program...
I'm interested in creating a eog signal filter using FPGA (HDL) but i dont have a sample data... and i like to have matlab program too...
Thank you carry on your good work
Data and code from my EOG experiments is now on my github!Delete
Thanks again for your detail write up!
I post some of my EOG results in my post. I got some similar results although the noise was noticeable and the baseline was drifted.
I checked out your work. Very nice! The low-frequency drifting of the the EEG baseline does indeed make it difficult to exploit the EOG signals. Please be sure to let me know if you do more work in this area!Delete
Hello, Chip. very instersting your experiment.ReplyDelete
There are many ways that i tink to go with EOG.
1 A EMDR device to help people from the PTSD.
2. Recording REM periods and replay this for recovery a dream.
3. Helping Psychologist in the interview.
Recently i know about de OpenBCI and it's been a great discovery for me.
First, thanks for your blog and for sharing your knowledge.
I've noticed that you write posts about measuring EEG, EOG, ECG...but as I understood from your posts and comments, you have background in electronics and informatics. So I would like to ask you whether you could recommend some books about topics from neuroscience for technical people. I did some search on the internet but I've got overwhelmed by the amount of books...so do you have your favourite? Thanks for the answer.
Hi Chip, do you know how to configure shieldboard ekg instead of using OpenBCI??ReplyDelete
Sorry, I don't know anything about shieldboard EKG.Delete
Hi Chip, How did you change that BPFilter to 0.02 Hz? Did you use MatLab to plot that data? The filters on the OpenBCI GUI that I'm using in Processing only have settings from 1-50 Hz etc. so I wasn't sure how to process the data to get those nice discrete signals you were getting! Sorry am new to this and just learning but love this post! Am working on a project to convert output signals from EOG to input for arduino that controls a blinking and moving eyeball! Any help/advice is welcomed and am grateful for!ReplyDelete
Yes, I used Matlab to process the data. That's how I was able to create arbitrary filter responses. The problem with Matlab is that it is so expensive (though a student version is "only" $99, which is a lot cheaper than the full version).
More recently, I've been using using Python, which is free. It can do all the same filtering and plotting that you see here. This post talks about which distribution I've been using...
And all of these posts have links to my Python code for reading and plotting OpenBCI data...
Hopefully this helps you!
can you explain your spi configuration ?
Is it possible to use a hacked mindflex headset for this purpose?ReplyDelete
Hello, your application looks very nice and stable too. My friends and me are trying to make a similar app,for a university subject, about tracking the eye blinking and i would like to communicate with you in order to make some questions about your work.It would be very helpful if you gave us some advice concerning the filtering and amplification of the signal. Anyway thank you for posting the whole thing on the internet and hope to talk with you!!ReplyDelete
I want to reproduce the result for a project. I was unable to find the OpenBCI arduino shield. The only one which comes close is this board - https://shop.openbci.com/products/pre-order-ganglion-board?variant=13461804483
Would that suffice?