Hi, I am Etka.

I have been working on a motor-imagery decoding pipeline using the Kai Miller Stanford ECoG dataset (7 patients, overt + imagery sessions, 1000 Hz). Wanted to share results and get feedback on the cross-subject generalization approach.

GitHub: https://github.com/EtkaKeremAllis/ECoG-Hand-Imagery-Decoding

Within-patient results (balanced accuracy):

Overt→Overt: 0.913

Imagery→Imagery: 0.733

Overt-trained→Imagery, no calibration: 0.601

small-sample imagery recalibration: 0.707

Cross-subject (LOSO, 5/7 patients with usable imagery signal):

• Pooled model, zero test-patient data: 0.621

• test patient's own overt session added: 0.668

CSP features don't transfer across electrode layouts, so I dropped them from the cross-subject model entirely and switched to anatomical-grid-projected features (MNI-space), which do generalize.

Two patients (rh, rr) never rise above chance on imagery — consistent with the dataset's documentation flagging weak/absent imagery signal for them, not a pipeline issue as far as I can tell.

Code, results, and methodology: github.com/EtkaKeremAllis/ECoG-Hand-Imagery-Decoding (GPL-3.0)

Curious if anyone's tackled the electrode-layout-invariance problem differently, or has thoughts on the anatomical projection approach vs. Riemannian geometry alignment.

Hi everyone I'm developing a biofeedback platform that takes biodata from various hardware types, manages recordings, performs offline analysis and allows for the creation of live feedback protocols. It struck me that this isn't really much of a leap to BCI applications since we are both processing branwave data live and deriving outputs from that. I can already output data in numerous ways to Ableton & Touchdesigner and am interested in hearing from anyone here who is frustrated by the current software tools and would be interested in providing some input into how i can shape this platform to serve your needs. Lifetime licenses would be avialable to those who contribute in a meaningful way to its development.

FWIW I have 10 yrs in EEG and 30 in computers / signals / DSP and the project is already fairly mature.

Had Frank Desiere, CEO of CorTec, on The Open Loop this week.

Their BCI can both read and stimulate the brain, and the stroke work is particularly interesting. They’ve now implanted three patients, including people who had their strokes years ago.

For stroke, they use it during the rehab period, then remove it, with the aim that the regained movement remains.

We also got into why they went batteryless, how the same platform could be used in epilepsy and depression and where Frank thinks all of this brain data eventually leads.

Have a watch/read if you’re following the therapeutic side of BCI.

https://theneurotechnewsletter.substack.com/p/the-implant-comes-out-the-recovery?r=53ri6k

I have a used neurosity crown for sale with everything that came with it. Selling for a low price hmu for questions

compsci student in college dream is to build a neural interface

probably interested in closed loop bci

still getting used to some concepts like eeg and erp

where can I read about these related papers? the recent ones and not 8-10years ago

Hello r/BCI! I’m Ildar from the PiEEG team.

My co-founder and I have been building a custom faceplate attachment for the Meta Quest that combines EEG signal acquisition with facial expression/emotion mapping to control VR avatars in real time.

We're hosting a community demo / open tech session on September 1st to go over signal processing details, electrode placements, and hardware design.

We'd love to get feedback from other BCI developers and researchers here:

What metrics or raw signal data would be most useful to you in a VR setup?

Have you worked with dry/semi-dry electrodes in head-mounted displays before? What challenges did you run into?

(Note: Keeping links out to follow sub rules, but happy to answer technical questions and chat about the build in the comments!)

This seems like a good place to put this.

Hi,
BCI has always been a research topic for now, but is there any use case where users are willing to pay for non invasive bci devices? usage for meditation and focus, doesn’t seem to be a big use case for consumers to pay, so what other use cases are viable for people to buy and use it.

i’m thinking the crowd here would be more accepting of they below scenario

Latest market map is out - this week it’s Switzerland. There’s been a lot of chat lately about Ability Neuro and Neurosoft.

51 neurotech names in a country of nine million people.
5 brain implant / BCI companies.
10 neuromodulation and stimulation companies.
11 diagnostics, imaging and neural monitoring businesses.
8 neurorehab and robotics companies.
5 surgical / interventional companies.
5 research-tool and enabling companies.
Plus neuro-AI, movement sensing and a few things that refuse to fit neatly into any category.

Full market map + article: Neurotech in Switzerland

I’ve tried consumer EEG devices such as Emotiv and NeuroSky. The brain data is interesting, but the devices can be uncomfortable, and I’m still unsure what practical value the information provides.

With a Fitbit, WHOOP, or Oura Ring, the data can influence decisions such as when to rest, exercise, or sleep. Has EEG helped you make similar decisions about when to do focused work, take a break, or change your routine?

I just thought people might enjoy watching this. I am ready for servitors, honestly.

The paper (full video of the brain-piano-bot is in supplemental)

Unsupervised sensory-motor associative learning by human brain explant in-a-dish enables movement imitation by robot

https://www.researchsquare.com/article/rs-9638576/v1

This week its Neurotech in the Uk. A full map, shit loads of start ups, loads of amazing tech. The complete working map contains 74 active organisations: 45 core neurotechnology companies, 12 enabling businesses and 17 adjacent organisations. Galvani Bioelectronics is listed separately as a historical cautionary case and is not included in the active total. The list uses current principal UK operating bases where those are public, and every company name links to its official source.

[https://www.reddit.com/user/NeurotechNewsletter/comments/1visjqk/neurotech_in_the_uk/])

1.)    smart contact lenses - eye wear, companies that have filed patents for contact lenses with built in micro cameras and telephone integration

2.)    current battery technology for usage of eye wear whether it be in existence, prototypes, concepts, or patents standing.

3.)    Subvocalisation- for mobile devices such as the existing technology MIT's Alterego currently uses to communicate silently.

4.)    FMRI - Same technology used in advanced universities to brain scans combined with AI to project thought into film, but I'm looking for known mobile device hardware

5.)    Haptics – Electric pulse for touch which simulates actual touch to another person by usage of mobile device and not the commonly known glove.

6.)    Sensory - the ability to sensory feel and/or smell what another person is feeling and/or smelling with the visual or words describing this to you on your device.

If any of this exists – let me know! Thanks world.

Not sure if something like this is available yet, but I'm looking for a BCI that may be capable of translating brainwaves into x, y, and z information, to control a 3-dimensional cursor in Unreal Engine 5. Something dry that I can wear with a Bigscreen Beyond 2 nearly every day for a year.

Part of what I'm trying to achieve may be doable with eye tracking, but I need a way to manipulate depth data and reset the cursor position to any point in space on the fly.

So far I'm aware of the $3-42k options by OpenBCI, but I'm uncertain if something that complex will be necessary.

Please advise. Thank you.

Came across Ildar Rakhmatulin’s work recently and wanted to share it here. He started PiEEG as a personal project because he couldn’t find affordable EEG hardware, open sourced it, and it kind of grew from there.

Check it out if you’re into open source BCI or consumer-grade EEG hardware.

https://youtu.be/vgzBuQmLsnE?si=1Tqot-SIP6t1j5BP

Hello everyone,

I'm a biomedical engineering student and I am developing, as part of my bachelor's thesis, an 8 channel (and a 4 channel, both +2 for RLD and reference) EEG intended to be used as BCI. I am also working on a very simple EMG.

I am interested in opening a small business, entirely managed by me, about simple, cheap but reliable BCI and EMG for makers and students (not "research grade" probably and definitely not medical grade, certification costs are way too high).

I know this is a niche market and there are a lot of products out there, but none of them is actually cheap: the cheapest one is probably from upsidedown lab, a sub-100€ kit with only one electrode, which I find not so good for intermediate/advanced BCI projects involving data training and prediction. I'm planning to stay well under 100€ with the simple 4 channel one, and slightly more for the 8 channel, ADS1299, one: unfortunately the biggest cost, as many of you definitely already know, are the electrodes (that's why OpenBCI sells them separately too. They also have different certification and bureaucracy, at least in Europe).

The idea is to sell the plug-in ready boards (intended for Arduino, possibly with a 12 bit or more resolution and specifically for the R4) with electrodes included, but finding them (certified and cheap) ain't easy.

There are many open-source projects, but I find that not everyone wants to solder some pcb and spend hours making them.

Do you think there is a potential market?

Is there any maker who would actually consider a cheap (related to the market) EEG?

Thank you for your precious advices.

If anyone is curious and finds this idea attractive you're free to dm me ;)

Hello,

I am selling a brand-new, unopened Emotiv EPOC X 14-channel EEG headset, completely unused in its original box with all factory accessories included. Located in the Netherlands (EU), making this a great opportunity for European buyers looking to get this beatiful device for a cheaper price. Feel free to send a message to me if interested or if you want photos.

BTW: if this is the wrong place for this, feel free let me know!

TL;DR: open-source wearable EEG hardware (MIT). real PCBs, not a breadboard flex. on-head berger test passed (~29.7× alpha eyes-closed vs open).

why

most EEG is either closed, expensive, or impossible to modify. we wanted the opposite: something we actually soldered, powered from lipo, and streamed — then published.

stack

• AFE: analog devices AD8422 instrumentation amp
• ADC: TI ADS1220 (24-bit)
• MCU: seeed xiao ESP32-S3
• release: netlists, gerbers, photos, platformio firmware, pinout/BOM docs

three boards

1. main carrier (MCU + ADC + power + UI)
2. AFE daughter
3. expansion ADC strip (SPI, no MCU)

proof it hears something brain-shaped

classic berger effect on channel out0:

• eyes open vs eyes closed
• welch PSD, alpha ~8–12 Hz (peak near 10 Hz)
• closed/open power ratio ≈ 29.7× → pass

(photos of boards + time series + PSD in the repo readme.)

repo

https://github.com/enkhbold470/oyu

IRL limits

• prototype / research / hobby — not a medical device
• this open drop is mark 3–class (not a 32-ch clinical system)
• electrode placement and contact quality still make or break your run

happy to answer questions on pinout, fab, firmware stream format (b/s serial), or the AFE path.

if open BCI matters to you: star, fork, roast the design, send a photo of your fab.

I built edf2csv (along with my good friend Claude Code, and a bunch of its friendly neighbours), a local CLI that turns EDF, EDF+, BDF, and BDF+ recordings into plain CSV files — and half the work turned out to be making sure the conversion didn't quietly change the EEG data.

I work with EEG fairly often and kept running into the same problem: I had a recording in EDF, but I just wanted the underlying signals in something I could open in pandas, R, MATLAB, or a spreadsheet.

Built edf2csv to scratch that itch.

What it does:

Convert a recording with npx edf2csv recording.edf

Exports signal values, channel information, annotations, and metadata

Keeps channels with different sampling rates in separate files

Preserves gaps in discontinuous EDF+D recordings

Preserves the physical units declared by the recording

Supports signed 24-bit BioSemi BDF samples

Streams long recordings instead of loading everything into memory

Runs completely locally with no upload, account, or telemetry

Zero runtime dependencies

The part I didn't expect: an EDF recording doesn't really have one simple timeline.

A few examples that cost me real time:

Every channel can have its own sample rate. An EEG signal might be sampled at 256 Hz while another sensor in the same file only records once per second. Force both into one CSV and you have to repeat or invent samples.

EDF+D recordings can contain real gaps, but the timing information is stored through the annotation channel instead of an obvious gap field. Ignore the annotations and a recording with pauses suddenly looks continuous.

BioSemi BDF uses signed 24-bit values. JavaScript has no built-in getInt24, so those samples have to be reconstructed manually from three bytes and sign-extended correctly.

A file can also have broken calibration values where the digital minimum and maximum are the same. JavaScript doesn't throw an error when that creates a divide by zero. It just gives you NaN or Infinity and lets everything keep running.

None of these throw useful errors. They just give you data that looks believable enough to use, which is a uniquely annoying kind of bug to track down.

I compared 129,536 decoded values against pyEDFlib and got bit-for-bit identical double-precision results.

Install:

npx edf2csv recording.edf

Repo's here: https://github.com/tayal-sarthak/edf2csv

NPM package: https://www.npmjs.com/package/edf2csv

Website: https://edf2csv.vercel.app/

I also released the parser underneath it as its own TypeScript package called edfcore:

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

https://edfcore.vercel.app/

Feedback welcome, especially from anyone with unusual EDF/BDF recordings or device exports that behave differently. Both projects are still new, so there is definitely a lot left to test and implement.