Some background: In the past when I've had PVC pipes near the Cyton, the static charge build-up on the PVC affected the recordings. So I'd like to avoid that kind of thing happening due to the plastic of the waterproof case.

I am working on a fun EEG demo for undergrad students with the following set up:

• Electrodes: Headband with dry comb electrode over the visual centre and dry flat electrodes on the forehead
• Software: I use the OpenBCI GUI with the spectrogram widget
• Effect I am showing:1. eye blinks as noise in the forehead electrodes, 2. alpha waves with closed eyes over visual centre and alpha-blocking when opening eyes

I used the Cyton and the Ganglion to test this set up each with their USB Dongle. I noticed that with the Cyton the effects are much clearer visible. Can this be due to differences in gain? I noticed with a little less gain I have better results with the Cyton. With the Ganglion the gain cannot be changed right?

Can you give more insights into the differences of Cyton and Ganglion for EEG measurements. What measures can I take to improve the results with the Ganglion as I want to use the Ganglion for future student projects.

Thanks a lot in advance!
Best, Louisa

What do you think? Any recommendations for me? Thanks

I dont know if it is usefule or not sending the setup pictuers.

Also I turned off any devides to reduce the EMF as much as possible.

I contacted the openbci support and said mention the problem here.
@retitut @richard @openbci.com @wjcroft
I will greatly appreciate your support and consideration.

Thanks.

Currently I'm planning to do a small project where I could combine programming, engineering and EEG.
I'm a software developer and a mechanical engineer and recently got interested in brain activity and neuroscience.
I've been wondering if it's possible for me as an amateur to get imagery from the motor cortex areas that correspond to both arm movements. I would like to write a software to do some experiments using both arms. I'm curious how many electrodes would be sufficient for that task.
I'm wondering whether it's not too difficult for a start.
It's my first project in that area so I don't have experience in EEG. If you have any advice or ideas I would be happy to hear it.
My primary goal is to get as much knowledge about EEG and brain activity as possible. And to combine my experience from other areas with neuroscience.
But my ultimate goal is to gain experience in the subject and then find a team, a project or a company that needs someone with my qualifications. After years of experience in industry I have realized that I want to work on innovative and creative projects.
Thanks in advance for your advice.
Pyotr

I tested OpenBCI Ultracortex with ThinkPulse and I believe that it looks fine when I observe EEG on OpenBCI_GUI via serial communication. I hope to use higher sampling rate than 250Hz, and so I used micro SD. I could use micro SD without error but I was surprised that amplitude of waves was obviously weaker than it recorded by serial communication.

I did simple experiment to show this issues. Would you check my attached video?(or an
image) In this video, a left side window shows data recorded in micro SD and a right side shows data via streaming. In this experiment, a subject meditated in 3 minutes. Of course, sampling rate was 250Hz. The data was recorded by both of micro SD and streaming at the same time using OpenBCI_GUI. These data should have been the almost same as each other. However, you may find the amplitude of waves in the left windows, recorded in micro SD, quite lower than the right side of ones, recorded via streaming. I guess that the amplitude of data in micro SD is nearly 1/3 of the amplitude of streaming data but the shape of waves looks the same as each other.

I assume when I record the data in micro SD, I observe small amplitude of waves.
I do not know why it happens. It is difficult to observe significant spectrum from such poor data.
I would be happy if I received your response.

(1) When using Gel free cap and Cyton Daisy, along with brainflow, which gain value should be set? the default seems to be 24.
(2) with the same settings, what should be the maximum impedance values for conducting research experiments that are suitable for creating large dataset and publications.
(3) When comparing the OpenBCI devices to other devices, is it a good practice to match the same gain values regardless of using semi-dry electrodes (wireless device like cyton) vs wet electrodes (wired device)?
(4) Is there any other things that I should consider? the overall goal is to compare the results of wireless device vs wired device data and produce a large dataset for machine learning application.

Thanks!

I am using the Cyton board with 6 channels and IDUN Dryodes to collect data to train a CNN to recognize 4 different forearm positions as I livestream. I used the Brainflow and tensorflow libraries to collect data and build the CNN and then stream data to test. I have trained several models trying various parameters, data collection methods and CNN layers. I am able to classify at most 3 motions accurately but when I use the same model and reapply the dryodes the next day, it no longer works.

Here is a video of what I had last night (does not work now):
https://www.youtube.com/shorts/7bjFltX8CKI

I want to figure out what might be causing this so was wondering if anyone had advice on things I should change. I am attaching images of the 4 positions I would like to classify and of my electrode placements as well as how I set up the board (please excuse the makeshift look):

I have used the plastic feet to screw the board down to the wood block (one screw is not in because it went through angled as I did not bother to pre-drill). I taped the cables and battery pack to the block to prevent as much motion from the cables to the pins as I move my arm. Note that the channels connected are 3 to 8 (6 total).

My methods to collect data were as follows:

1. Hold one of four positions and stream data form the board
2. Create a time series sample of 140 every 0.02 seconds using Brainflow get_current_board_data(140) (This allows me to overlap samples but with enough delay that I don't have completely identical samples several times)
3. Filter the sample with Brainflow perform_bandpass on each channel with an order 4 Butterworth bandpass filter from 25Hz to 240Hz
4. Rectify the sample
5. Denoise the sample with Brainflow perform_rolling_filter on each channel with median and period 3
6. Clip the first 20 elements in the sample resulting in 120 timesteps (I do this because the filters seem to leave a huge spike at the start of each sequence)
7. Collect all the samples into a 3D array of 5000 samples (I held the position for about 100 seconds, resulting array shape: 5000, 120, 6)
8. I collected 10000 samples for each of the 4 motions over 30 minutes and scale the combined training set of shape (40000, 120, 6)

I then pass this to the CNN with the following architecture:

After the model is trained and saved, I import the trained model in a separate file to stream data and perform the exact same steps on the sample (collect 140 timesteps, filter, rectify, denoise and clip) before passing it through the neural network.

I know that the IDUN Dryodes mention that the impedence decreases over time. I assume this is because as you sweat, the salt water increases conductivity between skin and electrode? I record the time between once I have finished applying the electrodes and when I start and finish collecting samples every time. Typically I try to wait 1 hour after applying the electrodes and then I collect samples for an hour.

Again, almost always the models are able to classify at least 2 motions (the clenched fist always works) and sometimes 3. So far I have been unable to do all 4 and no matter the model, my test the next day on the same model completely fails.

Also, I have read online and in the forums that EMG data is best filtered from 25-400Hz, which is why I used the 25-240Hz bandpass. I do not have the WiFi Shield to increase the sampling rate and it is not an option for me at the moment.

I would like to know if I am doing things wrong and how to change them. Specifically, for my purposes, is the bandpass filter set appropriately? Are the electrode placements suitable?

I am going to try a few different methods now. I am going to collect 500 samples at a time (hold position for 10 seconds) because I have seen studies that say the muscle fatiguing over time greatly affects the EMG waveform. I am also going to change 1 of the electrode placements from the bicep to the forearm because I think I will do different hand positions. Instead of supination and pronation, I will try wrist flexion and extension.

Thank you for any advice if possible.

Namely, I am talking about modifying this device
a program to interact directly with the brain without using do
this constant touching

My older brother has a rare disease and this device helps
him in the development / help of his thoughts, but the matter is of this type that
he keeps taking it off and on.
My question is, is it possible to set this up in a program that
is available so that it does not unfasten, only I could automatically
turn it off in the program?

Thanks,
Raf

We wanted some suggestions regarding dry electrodes. So we are about to start collecting data using Cyton Daisy (16 EEG channels) for our research and we are using Spiky electrodes. First question is how can we replace the tip of the electrodes, for example, convert spiky to flat ones. Also, when we are checking impedance the spiky electrodes hurts and we are concern that participants won't be comfortable with that. So I found these two alternating options https://shop.openbci.com/products/5-mm-spike-electrode-pack-of-30 and https://shop.openbci.com/products/thinkpulse-active-electrode-kit. Out of these two which one will be the most comfortable and will be good for our research?

Also want some suggestions on how to make the impedance checking less painful for the participants?

Sincerely,
Shyamal

Has anyone else had this problem? Does anyone know how to fix this?

Thanks,
Sam