• 5 channels in a ring round the upper forearm, at 40 % from the medial epicondyle to the
  ulnar styloid (after Farrell & Weir 2008),

• 3 on the hand: first dorsal interosseous, abductor pollicis brevis at the thenar edge,
  abductor digiti minimi at the hypothenar edge, pairs 2 cm apart.

The palm stays bare (grip pressure), and the hand's electrodes would sit in a tight glove:
dry 316L steel studs in moist rings. The goal is decoding finger and thumb movement and
force for control.

Questions:
1. Off the palm, which thumb sites add information beyond APB and FDI? Is a site along the
radial side of the first metacarpal (opponens, under APB) worth its own channel, or is
it APB again?
2. Does a forearm ring see flexor pollicis longus well enough for thumb pressing, or should
one forearm channel go over FPL on purpose?
3. 5 forearm + 3 hand against 4 + 4: any data or experience?
4. Electrodes inside a glove on the back and edges of the hand: how stable is the contact
during grip?

The layout: https://github.com/Project-NIC/NIC-Crazy_Ivan/blob/main/bogatyr/CONSTRUCTION.md
Thanks for any pointers or papers.

We've recently been experimenting with using a light sensor connected to the Cyton board to record visual stimulus onsets during EEG experiments.

The setup is working well, but we've encountered what appears to be a limitation of the BDF recording format in the OpenBCI GUI (v6.0.0-beta.1).

Setup

OpenBCI Cyton, 8 channels
Windows
Light sensor connected to D12 (A6)
Analog Read mode enabled in the GUI
EEG/EMG recorded simultaneously with the light-sensor signal

Observations

The light-sensor signal is clearly visible in the Analog Read widget during acquisition.

We have also confirmed that the sensor signal is saved correctly in both the native OpenBCI RAW format and the BrainFlow CSV format, where it appears in column 21 (Analog Channel 1).

However, when recording in BDF format, the resulting file contains only 10 channels: eight EEG/EMG channels and two additional channels labelled "Accel X" and "Accel Y". D12 signal does not appear to be present.

We can currently work around this by recording in OpenBCI RAW or BrainFlow format and converting the data afterward, but being able to save EEG and analog trigger signals together in BDF format would be particularly useful for experimental research, as BDF can be imported directly into common EEG analysis software such as EEGLAB.

Thank you for developing and maintaining the software! We're happy to provide example recordings or perform additional tests if that would be helpful.

Best regards,
Richard

My worry is motion artifacts: changing capacitance, changing loop area, and triboelectric
charge as the strip and the fabric move.

1. In your experience, did motion artifacts come mostly from the leads or from the skin
   under the electrode?
2. Shield grounded vs driven at the body's common-mode voltage: did driving it help?
3. Passive leads vs an op-amp follower at each electrode: worth it for EMG?
4. Steel electrodes without gel: what offsets and drift have you seen?

The design: https://github.com/Project-NIC/NIC-Crazy_Ivan/blob/main/rubaska/CONSTRUCTION.md
Thanks for any experience.

On an anatomical atlas we tried one electrode per muscle belly, two rows of oblique pairs round each limb, and on the trunk pairs from one muscle to its neighbour and pairs scattered by a search to survive the suit shifting a few centimetres. None convinced us; each try came out different, and honestly we do not know how to judge a layout. Nobody puts a suit on the same way twice, so we want one that tolerates a shift.

What we think so far: a pair's lean counts against the fibres, not the body, so on an oblique muscle a level pair is already oblique; a pair across several neighbouring muscles only where it pays, and on the limbs probably single muscles or blocks of muscles that work together; the erectors as six blocks at least.

How would you lay it out? Where single muscles and where blocks, how to divide the erectors, and where on the trunk to tell the rectus, the obliques and the erectors apart? We can share pictures of what we tried.

https://github.com/Project-NIC/NIC-Crazy_Ivan

Got some questions:

1. How do I get the angle between two IMUs on one strap without drift building up over 10 minutes (filter, calibration pose)?
2. 6-axis or 9-axis for slow movements indoors? Which IMU under ~$15 would you pick?
3. How would you connect 4 IMUs to one ESP32 (I2C multiplexer, SPI)?
4. How can I check angle accuracy at home without a lab?
5. Is there open-source code or a library that computes the angle between two IMUs on an ESP32 that I can start from?

A one line answer or a link helps. I'm also looking for 1–2 people who'd like to review my ideas now and then, or join later as a collaborator (student project, unpaid). If that's you, message me, I'm open!
I'm Viktoriia, from Ukraine

For my undergraduate thesis I wrote a Linux kernel driver for the TI ADS1299 (and the ADS1299-4 and ADS1299-6). It is meant for two kinds of boards: standalone ADS1299 front-ends with no controller of their own, and boards where a Linux-capable processor (an MPU, such as a Raspberry Pi) talks to the ADS1299 directly over SPI. It does not apply to boards that put a microcontroller (MCU) with its own firmware between the ADS1299 and the computer, such as the Cyton as shipped.

Repository: ads1299-linux on GitHub

It is an IIO driver, so the channels show up in sysfs and samples are read from /dev/iio:deviceX. It supports:

• all data rates (250 to 16000 SPS) and PGA gains, per channel;
• buffered acquisition driven by the DRDY interrupt, with timestamps;
• lead-off detection, reported as IIO events;
• the internal test signal, shorted inputs, SRB1/SRB2 and the bias drive;
• the temperature sensor and supply monitors;
• a self test at probe.

It comes with a devicetree overlay for the Raspberry Pi and builds out of tree (plain make or DKMS) on 6.6 and later; it has run on 6.6, 6.8 and 6.18.

It also works without a devicetree and without a DRDY interrupt. On my own board, an STM32MP257F-DK running ST's Linux 6.6, and on an x86 PC, the ADS1299 sits behind an MCP2210 USB-to-SPI bridge: a HID driver for the bridge registers an SPI controller with the ADS1299 on it, and the driver reads each conversion on a host timer. On the PC that gave 10 s at 250 SPS with no lost samples; on the STM32MP2, with an earlier version of the driver, 30 min at 250 SPS lost 0.67 % of the samples on the USB link. The bridge driver is not published yet.

All features on one ADS1299 were tested on the Raspberry Pi 4 (Linux 6.18), the test signal on every channel within 0.5 % of the datasheet value, and lead-off events on open inputs, the bias drive, GPIOs, clock output and power down. Daisy-chain was not tested. The README lists exactly what was measured.

I've also submitted ADS1299 support to the mainline kernel. Following the review, it is being reworked as part of the existing ti-ads1298 driver, so the mainline interface will differ in some details from this out-of-tree driver.

Only one board drives: PD_BIAS on, BIAS_SENSP = BIAS_SENSN = 0xFF (its own 16 inputs),
1 MOhm || 1.5 nF in the loop. The other fifteen have the bias amplifier powered down and
their switch open, as TI's datasheet (SBAS499C, 9.3.2.4.5) and an answer on TI's forum
suggest for several devices.

Questions:
1. Has anyone held a whole body, or several limbs, with one bias drive that senses only one
board's inputs? Did the far boards stay in range, and the mains low?
2. Where would you put the one driven bias electrode on a whole body: on the trunk, or does
it matter little?
3. Cyton + Daisy: how is the bias shared there, and has anyone run several Cytons on one
person?

The design: https://github.com/Project-NIC/NIC-Crazy_Ivan/blob/main/rubaska/HARDWARE.md
Thanks for any experience.

• Ganglion 4-channel
• MacBook Pro M2 running macOS Sonoma
• OpenBCI GUI v6.0.0 Beta 1
• USB Dongle 112

1. Connection issues
   I'm having a lot of trouble with the USB dongle. The connection only seems to work if I keep the dongle very close to the Ganglion. If I have my MacBook on the desk and the Ganglion on my head (about 50 cm away), I start losing the signal.
   In general, the connection is very unstable, and the recording stops every few minutes.
   This doesn't happen when I use my Mac's built-in Bluetooth, but then I get a lot of packet loss.
   Has anyone experienced something similar? Could the USB adapter I'm using be causing the problem? My Mac doesn't have a USB-A port, so I need an adapter to connect the dongle.

2. Electrode impedance
   I'm also having trouble with two of the four electrodes (the two on the sides). Their impedance readings never seem to drop below 603, which makes me wonder if they're actually making good contact.
   I've tried pressing them firmly against my scalp, even through my hair, but nothing seems to help.
   Is there anything else I should try?
   I'm attaching some pictures of the errors, part of the console log, and a photo of the Ganglion powered on.

Any help or suggestions would be greatly appreciated. I'm still trying to figure everything out, so any advice would be really helpful!

Thanks in advance to anyone who can help me!

Brainflow start streaming
ERROR: Exception when starting stream
brainflow.BrainFlowError: Error in start_stream:GENERAL_ERROR
at brainflow.BoardShim.start_stream(BoardShim.java:1437)
at OpenBCI_GUI$BoardBrainFlow.startStreaming(OpenBCI_GUI.java:2764)
at OpenBCI_GUI.startRunning(OpenBCI_GUI.java:934)
at OpenBCI_GUI$TopNav.stopButtonWasPressed(OpenBCI_GUI.java:20645)
at OpenBCI_GUI$TopNav$2.controlEvent(OpenBCI_GUI.java:20484)
at controlP5.Controller.callListener(Unknown Source)
at controlP5.Controller.setMousePressed(Unknown Source)
at controlP5.ControllerGroup.setMousePressed(Unknown Source)
at controlP5.ControlWindow.mouseReleasedEvent(Unknown Source)
at controlP5.ControlWindow.mouseEvent(Unknown Source)
at jdk.internal.reflect.GeneratedMethodAccessor1.invoke(Unknown Source)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:568)
at processing.core.PApplet$RegisteredMethods.handle(PApplet.java:1309)
at processing.core.PApplet.handleMethods(PApplet.java:1456)
at processing.core.PApplet.handleMouseEvent(PApplet.java:2368)
at processing.core.PApplet.dequeueEvents(PApplet.java:2261)
at processing.core.PApplet.handleDraw(PApplet.java:2104)
at processing.opengl.PSurfaceJOGL$DrawListener.display(PSurfaceJOGL.java:840)
at jogamp.opengl.GLDrawableHelper.displayImpl(GLDrawableHelper.java:692)
at jogamp.opengl.GLDrawableHelper.display(GLDrawableHelper.java:674)
at jogamp.opengl.GLAutoDrawableBase$2.run(GLAutoDrawableBase.java:443)
at jogamp.opengl.GLDrawableHelper.invokeGLImpl(GLDrawableHelper.java:1293)
at jogamp.opengl.GLDrawableHelper.invokeGL(GLDrawableHelper.java:1147)
at com.jogamp.newt.opengl.GLWindow.display(GLWindow.java:782)
at com.jogamp.opengl.util.AWTAnimatorImpl.display(AWTAnimatorImpl.java:81)
at com.jogamp.opengl.util.AnimatorBase.display(AnimatorBase.java:453)
at com.jogamp.opengl.util.FPSAnimator$MainTask.run(FPSAnimator.java:178)
at java.base/java.util.TimerThread.mainLoop(Timer.java:566)
at java.base/java.util.TimerThread.run(Timer.java:516)
[ERROR]: Failed to start data stream. Please check hardware. See Console Log or BrainFlow Log for more details.
ConsoleLog: Opening console log as text file!

What came out:

• sEMG voiced → silent transfers (small but above the permutation null).
• EEG spoken → imagined does not, on Nieto or FEIS.
• Imagined speech collapses across days (Nieto: within-session 34%, leave-one-session-out at chance).
• Same-trial splits fake a transfer effect (FEIS 8.7%, p = 0.0002) that temporal-block splits remove (6.4%, chance). Same classifier, only difference is temporal proximity.

Repo with code, tables and feature caches (MIT, DOI on Zenodo):
https://github.com/gilbertoluporini-create/imagined-speech-baselines

Would love to hear from anyone with Ultracortex / dry-electrode experience on electrode repeatability across sessions. That is now the first gate of the project, before any decoding.

Dry comb electrodes are assembled on Medium 3-D printed cap. Headset print is rough and imperfect up close (printed by students at Uni), but reliable. Can be unassembled and shipped with or without cap.

Contains

• 8-Channel Cyton + 8-Channel Daisy
• Dongle
• 16 Dry Comb Electrodes plus extras
• Over 150 Foam Solid Gel Electrodes for EMG/ECG & storage case
• 1 rechargeable lithium battery & Adafruit USB charger
• Original holder for AA batteries
• Y-splitter
• Ear clips
• Clear board case
• 10 snap electrode cables
• Glass model head not included
  Contact me at candace@insidetheskin.com
  
  
  
  
  
  
  

I’m using BoardIds.CYTON_BOARD with the normal Cyton USB dongle and serial connection.
I record for 10 seconds and count the number of samples returned by BrainFlow.
When everything is working normally, I get approximately 2484 to 2485 samples in 10 seconds, with mean timestamp spacing around 0.004 seconds and a rate around 250.3 Hz.

The packet numbers are consecutive with essentially no missing packets.

However, when the system enters the bad state, the exact same script gives only about 617 to 618 samples in 10 seconds, with mean timestamp spacing around 0.0161 seconds and a rate around 62.1 Hz.

For example, one bad run gave:

Samples received: 617
Mean timestamp spacing: 0.0161045 s
Median timestamp spacing: 0.0169806 s
Rate from timestamps: 62.094 Hz
Normal consecutive packet jumps: 616
Abnormal packet jumps: 0

So the packet counter is still perfectly consecutive. It does not look like I’m simply dropping packets.

The strange part is that it can switch between the two states. Earlier runs with the same board, Mac, serial port, and BrainFlow script produced the correct approximately 250 Hz rate, while other runs produced approximately 62 Hz.

I have also occasionally seen BrainFlow fail to initialize with "board doesnt send welcome characters" and "BOARD_NOT_READY_ERROR:7", before working again on a later attempt.

I found older forum discussions explaining that USB or OS buffering can make timestamps appear irregular even though the Cyton itself samples steadily. However, this seems different because in the bad state I am actually receiving only about 617 samples during a 10-second recording instead of about 2500.

Also, the bad rate is suspiciously close to 250 divided by 4, which is 62.5 Hz, and the corresponding interval is about 16 ms.

Has anyone seen this combination before?

The OpenBCI GUI sometimes reports that data from the board is not changing or fails to start.

BrainFlow sometimes receives the normal approximately 250 Hz.

Other times BrainFlow receives only approximately 62.5 Hz.

Packet numbers remain consecutive in both cases.

Any suggestions for what I should test next would be greatly appreciated

This project started out as an intense curiosity and has been a tremendous amount of fun. This is not a professional project. I did this for fun. I have no real idea as to whether it is valuable to anyone. The project is called PRAYCG. It is an exploratory psychophysiology protocol and software stack for testing whether naturalistic narrative stimuli produce measurable EEG/autonomic state trajectories beyond low-level sensory entrainment and beyond analytic task demand.

This is not a clinical tool, not a diagnostic system, and not a claim that OpenBCI can prove consciousness, memory biology, or any metaphysical theory. It is best understood as an open-source methods workbench for stimulus preparation, acquisition logistics, timing/QC, and exploratory analysis.

What the protocol does
The core design is a three-arm naturalistic viewing protocol:

Phase-scrambled sensory control
The source video is scrambled so recognizable story/faces/narrative meaning are damaged while low-level audiovisual structure is partially preserved. This is meant to ask: “Is the response just light, sound, motion, cuts, rhythm, cue timing, or audiovisual energy?”
Target narrative
The intact narrative is watched naturally. This asks: “What happens when the story is allowed to land?”
Contextual Override
The same intact narrative is watched again, but with an analytic task, usually an upper-right number-cue running-sum task. Target and Override use the same cue-embedded video; only the instructions differ. This asks: “What changes when the participant is measuring/extracting rather than receiving?”

Current hardware stack
My current setup uses:
OpenBCI Cyton + Daisy
OpenBCI gelfree/gelless cap workflow
BrainFlow/LSL EEG stream
LabRecorder XDF recording
ALS-PT19 or similar light sensor for physical screen timing
Polar H10 R-R intervals
Vernier respiration belt
optional Faraday/shielded enclosure setup(see the box section on github)
PRAYCG2.0 protocol runner with structured event logs and self-report/confound reports

What the software release includes
The repository contains or will contain:
MediaPrep + StimulusFingerprint
prepares Target, Override, and phase-scrambled Control videos
embeds cue schedules
generates stimulus-side QC and exogenous regressors
supports predeclared anchor files for event-locked analysis

PRAYCG2.0 Protocol Runner
loads Control, Target, Override, cue schedule, and anchor file
logs structured events
includes pre-run display/audio calibration
includes branch-level self-report and confound reports
includes final reflection baseline
supports override task reporting
BrainFlow / ALS / LSL tools
OpenBCI streaming helpers
ALS/PT19 timing-sensor support
LabRecorder workflow documentation
stream checklist and timing-QC workflow
Master Comprehensive Analysis Suite
time-resolved feature extraction
artifact scoring
stimulus-regressor residualization
MRED/A-MRED endpoint-compression modules
MRED-Peak vs MRED-Resolution
DGA / Decoder Gate Availability
NUPI / Narrative Update Polarity Index
visualization tools
offline plain-English report generator
Documentation
full end-to-end SOP from media procurement to analysis
hardware- how to build a faraday cage for eeg studies
formula/module explanation report
Main analysis idea

The current primary analysis direction is called MRED: Meaning Recognition / Encoding Dissociation.
In plain language, MRED asks whether the system can distinguish:
immediate recognition of meaningful material, versus
delayed integration, after-state, or regulatory carryover.

The compressed endpoint layer currently separates:

MRED-Peak: acute anchor-locked recognition + delayed integration
MRED-Resolution: slower reflective/regulatory recovery after the stimulus
A-MRED: anchor-locked MRED candidate that must pass condition, artifact, and QC gates

I am not treating gamma as a “meaning biomarker.” Lower-gamma/high-frequency features are treated as artifact-sensitive candidate work-signal proxies. The pipeline tries to penalize or flag muscle activity, eye/forehead artifact, cue burden, sensory drive, respiration, confounds, and exogenous stimulus timing.

Why I am posting here
I am looking for critique from people who know OpenBCI, EEG acquisition, artifact handling, and LSL better than I do.

The areas where I most want feedback are:

OpenBCI Cyton + Daisy acquisition reliability
gelfree/gelless cap best practices
BrainFlow configuration
LSL stream naming and synchronization
LabRecorder setup and XDF recording workflow
ALS/PT19 photodiode/light-sensor timing validation
whether my start-pulse design should be longer or barcode-like
artifact control for gamma-band claims
EOG/EMG channel additions
jaw/forehead/eye artifact separation
respiration and HRV integration
whether the analysis suite is overbuilt and how it should be simplified
how to make the public GitHub repository easier for other OpenBCI users to run

Current limitations
The project is exploratory and self-run so far. Several pilot runs revealed exactly the kinds of issues that need better engineering:
timing-pulse detection needing a better physical ALS holder
BrainFlow/OpenBCI effective sample-rate variability
cue visibility/legibility issues in some stimuli
gamma-band artifact vulnerability
the need for EOG/EMG
the need for cleaner locked-anchor and timing workflows

I am not presenting pilot outputs as confirmatory. The point of the current release is to expose the machinery and invite technical critique before treating anything as strong evidence.

Links
GitHub repository:
https://github.com/hbanks87/praycg-open

Here is a list of the fun ideas/theories that I have come up with as a result of running this protocol. The following are not proven claims. They are the current working hypotheses made visible by the protocol,
software, and pilot failures/successes:

1. Meaning recognition and meaning integration can dissociate. A subject can recognize meaningful geometry without showing clean delayed integration under current proxies.
2. Peak-like meaning and resolution-like meaning appear to have different physiological shapes. Some events strike sharply; others settle slowly into afterglow.
3. Analytic extraction does not simply erase meaning. It can reroute, delay, tax, or alter the route by which
   meaning is accessed.
4. Sensory clarity and emotional impact are separable. Clearer semantic access can occur in one branch while stronger emotional absorption occurs in another.
5. Meaning requires access conditions. Sensory access, prior state-space, personal relevance, autonomic
   availability, low task burden, and low confound load all matter.
6. Baseline2 may be one of the most important windows for integration, but it is cumulative and cannot be attributed to Target alone without stronger design.
7. The phase-scrambled Control remains essential because sensory entrainment and meaning integration are not the same construct.
8. The suite is increasingly a theory of availability: whether a living system is available to decode, receive, and be changed by meaning.

Again, this is not a claim of proof. It is an open-source exploratory pipeline, and I am sharing it because I want the engineering and analysis assumptions challenged.

Thank you for taking the time to read this and have a wonderful day,
Hoyt

• 3D printed my own Mark III headset with the electrode holders and whatnot
• used normal regular jumper cables
• used comfort combs from the store (link)
• screwed on my own nuts and screws

Here's what I see on the OpenBCI GUI when I used my own 8-channel montage - one is with 50+60Hz filter and the other is without:


How do I know if my headset is working properly? I am very perplexed by the straight lines after filtering. Thank you in advance everyone!

Lot includes:

Cyton 8 channel board
USB dongle
9 electrodes (6 wired into home made head gear, plus three extra electrodes)
One set of electrode ear clips
home made head gear (not research grade, but it works for fun experiments
battery holder,
USB cable with Cyton size power plug (to run off external battery or wall charger)
original boxes and sticker

Greeting! I am a composer from Taiwan.

I would like to share my recent project that I have been working since 2025. This project focuses on the interaction between AI music improvisation (based on Somax2 of Max/MSP) and EEG (using Ultracortex). Here is the short demo of my work:

In this experiment, EEG signals are captured through the headset and used to estimate different mental and affective states, including arousal/valence and concentration. The EEG data are first sonified in Max/MSP, transforming brain activity into sound. These resulting sounds then interact with the parameters of Somax2 in real time, creating a continuous musical dialogue between brain activity, sound, and machine improvisation.

I have been experimenting with an Affective State widget for the OpenBCI GUI (v6.0.0-beta.1) and would really appreciate some feedback from the community! As I am not from coding background, so I created Arousal/Valence detection widget with help of Claude and ChatGPT to see if this is possible to work out...I have attached the photo of the widget in this post! [] (https://openbci.com/forum/uploads/editor/1r/hx9zyd3h6cbq.png "")

My specific questions are listed below:
1. Role of BIAS: I understand that the BIAS pin is used for noise cancellation, but could you explain in detail how it works?
2. Ground (SRB): How does the SRB function within the EEG system?
3. BIAS and SRB Relationship: What is the relationship between BIAS and SRB in the signal processing of EEG, ECG, and EMG?
4. Channel Implementation: I have experimented with combinations using BIAS and SRB with the active channels of the Cyton board. Could you explain how the 8 channels are implemented in terms of signal acquisition?
5. EMG Electrode Placement:Why must EMG active electrodes be connected in the “up and down” method to represent the potential difference of the muscle?
5.1. Why can’t we use the same top-channel method as in EEG?
6. Simultaneous Recording:Is it possible to perform simultaneous EEG and EMG recordings without interference?
7. Cross talk: While recording the EEG the EMG cross talk was occurring and during the measurement of EMG the ECG cross talk was occurring, why? to understand this please explain the circuit connections.

Thank you
Best regards,
Raghul Jayaprakash R

I'm Aaron, an independent maker/builder — sole developer of NeuralInducer.org, an open-source, non-commercial project exploring neural entrainment through light, sound, and vibration (Arduino-based devices).

I want to be honest upfront: I don't have EEG data yet. What I have is consistent subjective feedback from myself and several family members who use the devices regularly — reports of relaxation, easier sleep onset, and a "settling" feeling during the alpha protocol. That's it. No clinical claims, no "proof" — just a device that seems to do something, and a builder who wants to know if it actually induces measurable brain activity.

If anyone here has a Muse, OpenBCI, or similar setup and is curious to test it and look at the EEG, I'd love the feedback — good or bad. I can share build files, firmware, and full protocol details. Everything is open-source (CC BY-NC).

If you want to dig deeper, the full project — including the scientific background and research papers behind each protocol — is documented at neuralinducer.org.

If you're in the US and interested in testing the physical device itself rather than replicating it, I currently have two NeuroSync ALPHA units built (firmware v6.x — includes vibration/beep, Modo D) ready to send out — happy to work out the details with whoever's interested.

I'd genuinely appreciate any help with this project — whether it's testing, feedback, or just pointing out where I might be wrong.

Happy to answer questions here — my written English is decent, please bear with me if I take a bit to reply.

https://neuralinducer.org/

I am looking to sell my complete 16-channel OpenBCI setup (Cyton + Daisy + All-in-One EEG Cap Bundle).

I originally got this for a project that ended up changing direction. As a result, the equipment has been barely used and is in pristine, so I'm letting the whole bundle go for $1,800 OBO (Or Best Offer) (which is well below the ~$3.5k retail price) to move it quickly.

The equipment is in "like new" condition and has been very well taken care of.
Here is what’s included in the lot:

• Cyton 8-channel board + Daisy 8-channel module (attached)
• OpenBCI USB Bluetooth Dongle
• Complete EEG Electrode Cap (pre-wired)
• All standard adapter cables
• Bonus: I'm including a hard-shell tactical carrying case, a gel syringe, and a cleaning brush.

I have attached some pictures so you can see the condition.

Logistics: I am located in San Diego (CA). If you are local (e.g., around UCSD), I would love to meet up for a safe, in-person cash or Zelle transaction. If you are further away, I am happy to ship it via USPS/UPS Priority (we can figure out shipping costs in DMs).

Feel free to send me a direct message if you are interested or have any questions!

I would like to ask for help troubleshooting my Cyton setup. I have been wondering about this problem for a long time: why do the FFT plot and time-series signals from my EEG channels look almost identical across channels?

Current Setup

I am using:

• OpenBCI Cyton Board
• Ultracortex Mark III headwear
• OpenBCI GUI
• New electrodes and ear clip from Florida Research Instruments

I recently purchased new electrodes and a new ear clip from Florida Research Instruments to replace the old accessories, which were around 10 years old. I replaced six electrodes with the new ones.

Electrode Placement

My current electrode setup is:

• N1P: Oz
• N2P: O1
• N3P: O2
• N4P: Not connected
• N5P: Not connected
• N6P: Pz
• N7P: P7
• N8P: P8

In the OpenBCI GUI, I deactivated N4P and N5P because they are not connected.

Main Problem

When I look at the OpenBCI GUI, the time-series signals and the FFT plots seem almost identical across the active channels.

This makes me unsure whether I am recording real EEG activity from each electrode position, or whether there is a problem in the hardware, reference/ground connection, ear clip, or Cyton board itself.

Ear Clip Check

I read in some other forum posts that similar problems can be related to an ear clip or reference/ground issue.

However, I recently bought a new ear clip, so I am not sure if the ear clip is still the source of the problem. I also measured it using a digital multimeter, and the resistance was around 0.2 ohm, so the continuity seems good.

My Questions

If the FFT and time-series signals look almost identical across channels, what is the most likely cause?
Any guidance for troubleshooting this would be very helpful.

Thank you very much.

So I built edfcore, an open-source, zero-dependency TypeScript library for reading EDF-family biosignal recordings directly in browser and Node.js applications.

Install:

npm install edfcore

It supports EDF, EDF+, BDF, and BDF+ and exposes signal samples, channel metadata, sampling information, physical units, annotations, and recording timelines programmatically.

A few parts of the format ended up being much stranger than I expected.

EDF doesn't actually have one global sampling rate. Every signal can contain a different number of samples per data record, meaning EEG at 256 Hz can coexist with a much slower physiological channel in the same recording.

EDF+ annotations are also encoded through a special signal channel rather than being ordinary metadata.

BioSemi BDF adds another issue: its samples are signed 24-bit integers. JavaScript provides convenient readers for 16-bit and 32-bit integers, but not 24-bit values, so those samples have to be reconstructed manually.

The reason I separated edfcore into its own package is that I don't want every browser EEG viewer, BCI application, or Node-based research tool to have to implement these details independently.

GitHub:
https://github.com/tayal-sarthak/edfcore

Documentation:
https://edfcore.vercel.app/

npm:
https://www.npmjs.com/package/edfcore

I also maintain edf2csv, a separate command-line tool for researchers who simply want EDF/BDF recordings converted into accessible CSV files:
https://github.com/tayal-sarthak/edf2csv

Both projects are MIT licensed.

I'd especially love feedback from anyone building browser-based EEG/BCI tools or anyone with unusual EDF/BDF recordings that would be useful for testing.

Thank you!

• 21 real-time neural feature modalities in both sensor and source space
• Real-time single-trial decoding (CSP + any scikit-learn classifier)
• Adaptive feedback protocols: z-score, threshold, percentile, staircase, operant/RL-based, sham, multi-band, and cross-session transfer
• Online artifact-correction methods: ASR, adaptive LMS, GEDAI, ORICA, real-time Maxwell/SSS filtering for MEG
• Live visualization windows: Raw signal, NF feedback curves, epoch overlays, scalp topographies, 3D brain activity, TFR heatmaps, …
• External feedback output via OSC (Max/MSP, SuperCollider) and LSL outlets (PsychoPy, OpenViBE, BCI2000)
• BIDS-compatible session saving
• Full CLI

Use cases it’s built for: neurofeedback research, real-time BCI/motor-imagery decoding, and any closed-loop paradigm needing live feature extraction + adaptive feedback + artifact correction in one place.

Any feedback, issues, feature request, or contributions, especially from anyone doing real-time work who can stress-test it against their own hardware/paradigms are welcome!

Thanks!
Payam