My name is Rahela and am currently conducting doctoral research on how employees perceive wearable Brain-Computer Interface (BCI) technologies in the workplace, and I would love your input. I`d be grateful if you could take 10–15 minutes to complete my anonymous survey. There are no right or wrong answers, your honest perspective is what matters most. All responses are completely anonymous and voluntary. The survey explores employee attitudes toward wearable BCI devices at work, expectations and concerns regarding emerging workplace technologies, and how these innovations may shape the future of work.

Take the Survey here: https://1ka.arnes.si/a/0c999ab2

There are no direct material benefits for participants. However, your contribution will directly support the development of the first empirically validated guidelines for the ethically responsible implementation of BCI technology in organizations. Every response makes a real difference to my research.

Should you have any questions, please do not hesitate to contact me.
Thank you sincerely for your time and valuable contribution to this research.
Rahela

Disclaimer:
This research is conducted solely for academic purposes as part of a self-funded doctoral study. The study is entirely independent and has no affiliation with, sponsorship from, or representation of any current or previous employer. All analyses, interpretations, and conclusions are exclusively those of the researcher.

We are currently looking for OpenBCI users interested in making money selling their neural data and will be conducting a pilot study this summer. If you're interested, please feel free to reach out here or at my email ucabmk5@ucl.ac.uk. If you're not interested, feel free to leave some feedback and let me know why : )

These wrist mounted sleep trackers really aren't very good. EEG is the best.

I think some kind of adhesive electrodes with a light, small data logger would be the best idea. I mean when you are tossing around with your head on a pillow, any kind of low cost headware is probably going to get messed up.

Any hot tips on suitable amplifiers, data loggers, electrodes, analysis software? I'm willing to hack stuff together etc. and indeed really wish these scientists publishing these articles would share their source code. The world is a bit nuts, the road to getting promising technology/techniques out there is ridiculous. There are various products that claim they can do this stuff but they are never ready to roll, and probably never will be. We do it like this or it doesn't get done, basically.

Can anyone point me to hardware or DIY efforts towards this? We are in the process of adapting a Kernel fNIRS and making it portable - and would love to use OpenBCI systems.

Thanks in advance,
Cat

I'm new to this forum, and I'm looking for some advice. I’m a software engineer with a Bachelor’s in Computer Science, and I’m interested in eventually working in Brain-Computer Interfaces (BCI).

My company offers professional growth funding and is willing to cover the cost of individual college courses/certificates (excluding full degrees or startup expenses) or just opportunities in general. I want to use this opportunity wisely and take courses that will actually move me closer to BCI work. I am more interested in the implementation of machine learning (more specifically, deep learning) with the brain and how that can bridge a gap between people and their prosthetics. I've been interested in this since high school and it never really went away.

I’m trying to figure out which subjects matter most at this stage.

Some options I’m considering:

• Machine learning / AI (especially time-series or signal processing)
• Neuroscience fundamentals (neuroanatomy, electrophysiology, cognitive neuroscience)
• Biomedical engineering–related courses

I didn't take a biology course during college, as I already had a science gen. Ed done, and the last time I took a biology-like course was my senior year of high school.

For those working in or near BCI/neurotech:

• What specific courses would you prioritize first?
• Are there any classes you found especially useful (or wish you’d taken earlier)?
• Is it better to focus on math + ML first, or start building neuroscience knowledge right away?

My long-term goal is to work on software in BCI (nothing really specific in mind right now), possibly pursuing graduate school later, but right now, I want to make the best use of employer-funded coursework. Nebraska doesn't have many options, so it would most likely have to be something online.

Thanks — I really appreciate any guidance.

My name is Elon Paul Anozie Nnamdi, and I’m excited to join this community. I’m based in Nigeria, and I’ve recently started a journey into exploring Brain-to-Brain Interfaces (BTBI).

I’ve set up a GitHub repo called btbi-experiments
, where I’m documenting my ideas, early experiments, and long-term vision for this field. The project is called Synapse, and it’s focused on how we might extend Brain-Computer Interfaces (BCI) toward more direct brain-to-brain communication.

My interests include:

Exploring how BCI/EEG signals can be processed and transmitted.

Investigating possible steps toward multi-user neural communication.

Connecting with others working on signal processing, hardware design, and neuroscience research.

I’m here to learn, share, and collaborate. I’d really appreciate any guidance on good beginner-friendly experiments, open resources, or ways I could contribute meaningfully as I develop this project.

Looking forward to engaging with you all!

Best regards,
Elon Paul
(GitHub: NAZBROTECH
)

I'm looking at two studies as a reference:

First, this study, which used fMRI to capture DMN/PCC deactivation in meditation versus a focused task.
https://pmc.ncbi.nlm.nih.gov/articles/PMC4529365/

Second, this study, which used LORETA to capture DMN deactivation.
https://drfredtravis.com/Papers/final paper DMN and effortless.pdf

Basically I'm trying to replicate the findings of study 1, but as fMRI is prohibitive, I'm looking to use the LORETA approach of study 2.

Does this sort of thing seem plausible to put together with an openbci-based solution?

Thanks very much for any insight anyone can offer.

import time
import numpy as np
import brainflow
from brainflow.board_shim import BoardShim, BrainFlowInputParams, LogLevels, BoardIds
from brainflow.data_filter import DataFilter, FilterTypes, AggOperations
BoardShim.enable_dev_board_logger()
params = BrainFlowInputParams()
params.serial_port = '/dev/cu.usbmodem11'
board = BoardShim(BoardIds.GANGLION_BOARD.value, params) # added ganglion board id here
srate = board.get_sampling_rate(BoardIds.GANGLION_BOARD.value)
board.prepare_session()
board.start_stream()
eeg_chan = BoardShim.get_eeg_channels(BoardIds.GANGLION_BOARD.value)
aux_chan = BoardShim.get_accel_channels(BoardIds.GANGLION_BOARD.value)
print('EEG channels')
print(eeg_chan)
print('Accelerometer channels')
print(aux_chan)
SCALE_FACTOR_EEG = (4500000)/24/(2**23-1) #uV/count
SCALE_FACTOR_AUX = 0.002 / (2**4) 

while True:
    data = board.get_current_board_data(1) # this gets data continiously
    # don't send empty data
    if len(data[0]) < 1 : continue
    time.sleep(1)
    scaled_eeg_data = data[eeg_chan]*SCALE_FACTOR_EEG
    scaled_aux_data = data[aux_chan]*SCALE_FACTOR_AUX
    print(scaled_eeg_data)
    print(scaled_aux_data)

• a ganglion board with 4 channels around Oz
• An Arduino stimulus flickering LED bulbs at 12Hz

I have verified that I am getting data at 200Hz by dividing the number of samples by the amount of time. The sample rate is always near-perfect.

I have good connections with the electrodes. The impedance is always below 20 kOhms.

I have verified that my data analysis pipeline is good by running it on publicly available SSVEP data. Using the publicly available data, I easily see a peak in the power spectrum for the stimulated frequency. So I'm pretty sure my analysis steps are good. They are:
1. Highpass filter above 5Hz
2. Bandstop 59-61Hz (I am in the US)
3. Get power spectrums with DFT, FFT, Bartlett's Method and Welch's Method

I record my data using BrainFlow for Java. I have verified that my data is coming in the same way it does in OpenBCI GUI.

I analyze my data both in java and then export it and and analyze it in MATLAB as well, and see the same results.

I tried putting a makeshift faraday shield with aluminum foil around the OpenBCI board, but this was silly because it just prevented the bluetooth connection from working correctly. So I had to do my recordings with no EMF shielding, and I'm unsure if this could be the source of the issue or not.

My raw data looks really noisy. Even after filtering it, it never looks nearly as smooth as the data I've seen from other devices. The publicly available data I mentioned above is sampled at 256 Hz and looks much less noisy.

Attached is a picture of some of my data as well as a zip file with all of my data and code. In the data, there are markers in milliseconds. The SSVEP stimulus starts at the second marker and ends at the third.

There are a couple of specific concerns I have about the data.

• The raw data has constant jumps. It looks "rectangular" because it will hover around one amplitude for some milliseconds then suddenly jump to another, constantly.
• The filtered data has random spikes, probably reflecting those jumps from the raw data
• The power spectrums always look pretty much the same. In DFTs and FFTs, there are spikes at semi-regular frequency intervals in the power spectrum. In Bartlett's and Welch's methods, there is never any signals above 10 Hz apart from occasionally a bump at 20Hz.

Steps Taken:
* An 8-channel EEG was recorded at a sampling rate of 250 Hz. The electrode positions used were Fp1, Fz, C3, Cz, C4, Pz, O1, and O2. Stimulation was applied with an electrode on the forearm, delivering impulses at a fixed interval of 1 second.
* Blinks were identified as artifacts using the Fp1 electrode and removed through interpolation based on a threshold (mean + 1.75 standard deviations).
* The signal was bandpass-filtered from 1-30 Hz (as my research indicated that PREPs occur within this frequency range) and notch-filtered at 50 Hz (to remove powerline noise).
* I used the Fz, C3, C4, and Pz electrodes to enhance Cz using the Laplacian method. I have attached images showing my Cz channel ('Cz ohne verstärkung' is the raw one and filtered + enhanced is the plot at the top).

Challenges:
* Synchronization of Cap and Stimuli: A major issue I encountered was the lack of synchronization between the electrode cap and the stimuli. This leads to inconsistencies and makes the reliable identification of PREPs difficult.
* Identification and Extraction of PREPs: I have tried to manually identify the PREPs, but it was not possible. There is still too much noise present with a bandpass filter of 1-30 Hz, and nothing significant can be seen at lower frequencies. I also attempted using a wavelet approach, but the identified peaks are not always clear and consistent. Generally, I haven't fully understood the concept of wavelets, and I might be applying them incorrectly.

I hope someone in the forum can help me overcome these challenges and find effective methods for reliably detecting and extracting PREPs in my data.

Thank you in advance for your support!!
Best regards, Ilo

Here are two studies that suggest that more delay results in poorer neurofeedback training. They both had a method to test total delay, but for the life of me, I did not grasp it from their explanations.
https://pubmed.ncbi.nlm.nih.gov/33166941/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8913511/

Does anyone have experience with other suppliers or know of third-party vendors where I could source the necessary hardware? Any help or suggestions would be greatly appreciated!

Could someone provide a tutorial, because the exported csv data needs to be analyzed using Matlab, and it is easier to analyze the signal after it is smoothed.

The EOG stays with its midline elevated toward 2000 micro volts.

These are the filters I have

I have a ganglion with EOG set up on channel one, with one electrode on each outside corner of the eye, being referenced to each other. I have one analog ground on my mastoid.

I'd expect each sample to have a unique index. What's going on?

Sample from .txt file:
%OpenBCI Raw EXG Data %Number of channels = 4 %Sample Rate = 200 Hz %Board = OpenBCI_GUI$BoardGanglionBLE Sample Index, EXG Channel 0, EXG Channel 1, EXG Channel 2, EXG Channel 3, Accel Channel 0, Accel Channel 1, Accel Channel 2, Other, Other, Other, Other, Other, Timestamp, Other, Timestamp (Formatted) 2.0, 0.0, -10.411881476640701, -83.82611760683358, -76.86990368925035, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158299904E9, 0.0, 2024-08-27 22:35:58.299 2.0, 0.0, 20.08700266201049, 39.82993452809751, 39.0557752372697, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158299904E9, 0.0, 2024-08-27 22:35:58.299 3.0, 0.0, 1.4660407826304436, -57.12697182316333, -39.49708343204111, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158326373E9, 0.0, 2024-08-27 22:35:58.326 3.0, 0.0, 20.0832627620548, -41.09028081316501, -27.80615617055446, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158326373E9, 0.0, 2024-08-27 22:35:58.326 4.0, 0.0, 22.49923813343048, 36.32938816957176, 37.95998455025256, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158330895E9, 0.0, 2024-08-27 22:35:58.330 4.0, 0.0, 8.549411298707128, -23.621208120137453, -15.935713711194694, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158330895E9, 0.0, 2024-08-27 22:35:58.330 5.0, 0.0, 9.906994982622564, -70.9234627597034, -57.358845620416105, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158336272E9, 0.0, 2024-08-27 22:35:58.336 5.0, 0.0, 20.404894158244133, -2.898422465659678, 23.232258524745703, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158336272E9, 0.0, 2024-08-27 22:35:58.336 6.0, 0.0, 31.871427422389388, 21.590442444197834, 48.091373530216515, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158342368E9, 0.0, 2024-08-27 22:35:58.342 6.0, 0.0, 28.370881063863635, -50.466210002079606, -44.037321978248656, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158342368E9, 0.0, 2024-08-27 22:35:58.342 7.0, 0.0, 32.29777601733804, -26.639307384379208, -4.110150051303208, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158348886E9, 0.0, 2024-08-27 22:35:58.348 7.0, 0.0, 50.97483639605343, 68.9226162834093, 69.85385137237608, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158348886E9, 0.0, 2024-08-27 22:35:58.348 8.0, 0.0, 33.161692907102406, -22.56281643267721, -21.956952639855444, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.72481615835535E9, 0.0, 2024-08-27 22:35:58.355 8.0, 0.0, 33.651619801297784, -22.413220434449613, -18.632181579247117, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.72481615835535E9, 0.0, 2024-08-27 22:35:58.355 9.0, 0.0, 47.01802224293351, 57.1755905225873, 73.6648094272241, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158367885E9, 0.0, 2024-08-27 22:35:58.367 9.0, 0.0, 27.40598687529564, 4.514059246517718, 16.95296649914235, 0.12800000607967377, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158367885E9, 0.0, 2024-08-27 22:35:58.367 10.0, 0.0, 24.653420507907867, -43.315521286800504, -29.855621346272528, 0.12800000607967377, 0.0, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158373942E9, 0.0, 2024-08-27 22:35:58.373 10.0, 0.0, 40.15530582424253, 31.78914962336421, 52.31746048014611, 0.12800000607967377, 0.0, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158373942E9, 0.0, 2024-08-27 22:35:58.373 11.0, 0.0, 45.15929196495563, 54.078953359276056, 68.27187369111925, 0.12800000607967377, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158383254E9, 0.0, 2024-08-27 22:35:58.383 11.0, 0.0, 30.270750241354108, -37.208264659158885, -35.562708678655326, 0.12800000607967377, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158383254E9, 0.0, 2024-08-27 22:35:58.383 12.0, 0.0, 47.11151974182576, 8.108103103935719, 25.913766792975366, 0.11200000531971455, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158398514E9, 0.0, 2024-08-27 22:35:58.398 12.0, 0.0, 64.27392063848674, 74.67084251530468, 89.66784133762121, 0.11200000531971455, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158398514E9, 0.0, 2024-08-27 22:35:58.398 13.0, 0.0, 37.69445165339857, -31.665732924826443, -26.355074987746775, 0.11200000531971455, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158404891E9, 0.0, 2024-08-27 22:35:58.404 13.0, 0.0, 39.92717192694545, -20.322616359218955, -5.778145431540906, 0.11200000531971455, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158404891E9, 0.0, 2024-08-27 22:35:58.404 14.0, 0.0, 54.42676405515522, 58.503255006857216, 69.89125037193298, 0.11200000531971455, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158429897E9, 0.0, 2024-08-27 22:35:58.429 14.0, 0.0, 51.748995686881244, 4.192427850328386, 22.387041134759784, 0.11200000531971455, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158429897E9, 0.0, 2024-08-27 22:35:58.429 15.0, 0.0, 35.84320117533207, -37.42143895663321, -20.490911857225, 0.11200000531971455, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158434747E9, 0.0, 2024-08-27 22:35:58.434 15.0, 0.0, 55.335559744387865, 38.22925734706223, 44.94237776752561, 0.11200000531971455, 0.6720000319182873, 0.7840000372380018, 0.0, 0.0, 0.0, 0.0, 0.0, 1.724816158434747E9, 0.0, 2024-08-27 22:35:58.434

In my research lab, we have acquired several EmotiBit sensors for studies involving human subjects. Initially, the EmotiBit EDA signals seemed to work fine, as shown in the example below:

In this image, the EDA signals exhibit good quality, with a visible tonic component and identifiable phasic peaks.

However, we have increasingly been registering poor quality signals, which mainly consist of noise or lack the expected tonic and phasic variations, as shown in the images below:

These images represent the raw EDA signals from different experiments. We have visually confirmed that data collection appears to be functioning correctly through the EmotiBit Oscilloscope.

Interestingly, we do occasionally register good quality signals, though this is inconsistent. Additionally, the EmotiBit sensors respond to movements in the bracelet or artificial manipulation, indicating that they are reading and transmitting data, but not reliably capturing physiological EDA responses.

In order to solve this bad quality EDA signal problem, we have tested several solutions, including:
• Changing the EmotiBit device (Two different Adafruit Feather M0 WiFi, and one Adafruit Feather ESP32 Huzzah).
• Replacing EmotiBit electrodes with new ones
• Updating and reinstalling EmotiBit software (versions 1.11.1, 1.8.4, 1.8.1)
• Measuring EDA signals from different individuals
• Adjusting the EmotiBit bracelet (tightening and loosening)
• Measuring EDA signals from different parts of the arm and hands
• Thoroughly cleaning the area where the EmotiBit is placed
• Trying to elicit high arousal and low arousal stimuli
• Using EmotiBit with low battery (30%, 10%) and high battery (70%, 90%)
• Directly feeding the EmotiBit with a power bank through the USB connection.

None of these attempts have resolved the issue.

Has anyone encountered this problem with EmotiBit? If so, were you able to resolve it, and what steps did you follow?

Now the best accuracy I can achieve if I take one file and divide it into test and validation sets (so that all external factors are the same) is approximately 70% (in the best-case scenario), but it is not enough for rehabilitation.
I have also tried to use OpenBCI GUI time series through LSL, but it did not make accuracy better, and in GUI at all no difference can be seen between rest and action when looking at graphs.

Is it possible to achieve better accuracy with this EEG headset? If yes, then what am I missing? What should I change, or add? What should be the bandpass filter range? How should the graph of rest and action states look?

Raw data:

All electrodes after preprocessing (with pink line showing rest - 0 and action - 1 states):

Does spikes before 1500 frame influences data overall? Should I remove them? Can they be detected already in raw data?
C3 electrode after preprocessing (yellow - action, purple - rest):

We are writing because we had some issues trying to use Cython + Daisy to simultaneously acquire EMG and EEG following this tutorial:
https://docs.openbci.com/GettingStarted/Biosensing-Setups/ExGSetup/

For EMG, we attached two electrodes to the arm following the recommended layout described in the EMG tutorial and we connected them to the top and bottom pins of N16P (Daisy). The remaining electrode (electrode) was connected to AGND. For a one-channel EEG, we employed the N1P bottom pin of Cyton board. The two ear clips were connected to SRB and BIAS bottom pins.

With this setup, we encountered the issue of ECG artifact being clearly visible in the recording, especially in the EMG channel. Additionally, when contracting the muscle, muscular activity bled into the EEG channel (see attached images). Neither of these activities was visible in the video available in the tutorial.

Another test conducted: Recording only EMG, connecting the elbow electrode to the BIAS pin. In this case, the ECG was not visible.

We wonder if there is a more optimal way to acquire EMG + EEG on the same board that we can use or if there is any error in the interpretation of the tutorial.

Thank you!

(The first image corresponds to the muscle at rest and the second after flexing the muscle.)

Initially, I thought it was some interference from the body, but I find I can reproduce this noise by simply leaving the electrodes placed on the desk. Spacing two differential electrodes far apart seems to cause this. Placing the two electrodes closer together or twisting their wires together completely eliminates the noise, and as far as I can tell the driven ground electrode has no effect on this specific noise. The issue is that even with twisted wires, the exact pattern reemerges when the electrodes are placed on skin.

I've noticed two main kinds of signal noise patterns. One is a consistent action potential-like signal that occurs at a rate of once per second. The other is a burst of high-frequency inconsistent noise that occasionally randomly intermixes with the repeated noise signal. These two noise patterns almost always occur together.

Pictured below is the repeat signal in 5 and 10 second intervals.

Pictured below is the burst signal in 5 and 10 second intervals.

Any idea what could be the cause or source of this particular noise?

Our thesis advisor wants to invest in OpenBCI products, but he needs to be sure that what we mentioned above will be fine.

Thank you in advance for your help!

Regarding we (RukBaan-mobile application for home maintenance services from Thailand) are developing business what is highly related to neuromarketing and biometric research. We already have some experience with hardwares (screen-based/B-alert EEG) from academic partners but never own for our lab, but now I and our team could convince to initially invest to build a lab, named Customer Lab and your product is seems promising to our experiments at a moment. However, we have some questions to clarify for getting more understand and also some suggestions from your side if you have.

1. If we order All-in-One Biosensing R&D Bundle, https://shop.openbci.com/products/all-in-one-biosensing-r-d-bundle, do I need to buy some more electrodes for 16 channels EEG experiment? (I saw that dry EEG comb electrodes are included but not sure if it’s good enough?)

2. Is it wireless EEG thru Bluetooth connection? coz I’m looking for using it with some degree of user movements. and what is an operation range that we should NOT be far between headset and Macbook?

3. Is there possibility to get a file for printing a headset if it’s broken in the near future?

4. I was looking for analysis with classification like I experienced with other commercialized EEG, especially 3 of them: attention/relaxation/cognitive or mental load. How could I get them from OpenBCI GUI or some way with minimal programming effort?

5. Do you have any agency in Thailand when we need some critical maintenance or another way to fix the problem after using it for a while, because we tend to use it in routine of business experiments.

6. When I would need an electrode cap?, is it ok to start with a headset for a moment?

Thx in advance, kindly experts