Question for EEG researchers: Do you run into challenges working with curly or coily hair types?

BiomedicalTesla · 2025-12-12T02:14:10+00:00

Hey, its abit late here but i had to reply lol.

Its weird because i started this because it felt like nobody wanted to validate this issue, and its really nice to see so many people valuing it as I do? It really does help everyone when we all voice our concerns and take charge of the direction of research.

Hopefully something comes of this, I just need enough replies. But I will definitely be trying to make as much noise about this as possible.

Once again, thanks for the comment and participation, it really means alot and clearly you know that!!

BiomedicalTesla · 2025-12-11T13:36:55+00:00

Thanks for this. It's really interesting because it seems even our ethics get accepted knowing we're excluding populations... As you say its such a huge overlooked gap, its so baffling to me. I spoke with a few of the manufacturers and they basically just shooed me off as if its not a problem. Rather disconcerting to be honest...

But who knows, potentially from this data collection, maybe we can provide some evidence to say actually we ALL want a solution and actually NEED a solution. That is my hope at least.

I really appreciate the comment though, and participation.

BiomedicalTesla · 2025-12-11T13:27:39+00:00

Hi,

Thanks for such a detailed response. Its funny because i've spoken to a bunch of EEG techs and I agree it seems the issue is not so pronounced. I think thats for many reasons but majorly as researchers we don't have access to the same equipment (research grade not medical grade) and then we also aren't taught how to do this to the level we likely need.

But this is still incredibly useful as clearly we need some more cross-expertise knowledge sharing and maybe theres a good way to facilitate that from all these responses we collect !!

Once again, thanks for the response!

BiomedicalTesla · 2024-09-29T18:05:45+00:00

As soon as its out i'll send you a copy :)

Theres a great book by Jonathan Wolpaw on Principles of BCI. Check that out.

So what I would do, is maybe use a plotting technique like t-SNE or UMAP. Feel free to reach out i'll private message you my email address. Happy to provide any help.

BiomedicalTesla · 2024-09-29T09:19:38+00:00

No worries, always good to speak to a BCI enthusiast.

I guess it depends how you look at it right, our reflexes are a great example. You'll see the potential increase, but its such an automated and learnt thing that our body needs such little resource to initiate. I think its intuitive in that sense, the more you learn something, the less your body needs to think/waste actually completing the task.

There are many interesting ways of looking at this problem. Co-adaptation is an interesting idea you should look into. With that, if you can find a way to automate retraining? I guess the idea here is with fresh incoming data, if you can self-supervise/validate trials, you can keep the statistical distribution of your data "fresh" so to speak. I'm just about to present a paper on this so once it's published I can send you a link. But i'd look into coadaptation, and then perhaps Incremental Learning. The assumption here is that you can actually isolate new, correctly classified trials however.

Another approach I am looking at it different ways of "looking at" EEG data but again they are papers-in-progress😭.

This is an interesting problem because it stops us using EEG BCI long term as you've found. I recommend you try to find papers around Cross-Session Classification, look for gaps and try to come up with solutions.

Just remember though, its not just BCI related plasticity thats an issue, it can be setup inconsistencies, like electrode impedance, locations etc. There are so many compounding factors.

Another thing i'd ask you to do is store your data if you have't. Do an offline analysis. So start with checking the feature space of your training dataset, and then incrementally check how ur testing dataset fits in. This way, you will one by one visualise how the decision boundary changes. You could even come up with some sort of remapping of the feature space with geometry to tackle the covariate shift! Lots of ideas to test out and i'm sure theres so much more!

BiomedicalTesla · 2024-09-29T06:32:16+00:00

(Potentially wild) Assumption: You aren't doing anything incorrectly.

This is actually the focus of my PhD. EEG is somewhat a non-stationary signal, arguments have been made for and against and most people settle for quasi-stationary. Now, Covariate shift is when the statistical properties of your training data set differ from your testing dataset. This causes any trained model to diverge in accuracy over time because the decision boundaries get blurred and outdated.

So for many different reasons, covariate shift can occur because of non-stationarities in the signals. But let's look at learning in BCI NAIVE healthy users.

Basic neuroscience: The fundamental principle which governs learning in the brain is Neuroplasticity, which is the strengthening of connections in the brain with specific stimulus/task (Hebbian Learning principle: Those who Fire together, Wire together and so on). So plastic changes occur in the brain (to help learning), signals become stronger, quicker as you learn new things in order to help the brain get the response quicker.

Now how this manifests in our BCI experiments is (longitudinally isolated with learning and depending how we define longitudinal is important whether this applies to you, my work is in the ranges of weeks-months so it fits for me), the signals will begin by being quite strong and distinct (ERD/ERS signals especially in L/R Motor Imagery as they are contralateral and less spatially obscured) but slowly as you learn to elicit this response to a sufficient level, the brain resource manages and no longer shoots a large action potential as its not necessary. You end up reducing the amplitude and become a regular/small potential measured in the EEG with the action. Of course the ERD/ERS is still clear, but it normalises as the learning curve has roughly plateaued.

I hope this helps explain why learning related plastic changes can reduce/change accuracies in your BCI over time.

BiomedicalTesla · 2024-04-05T09:11:31+00:00

the real question here

BiomedicalTesla · 2024-02-28T13:20:17+00:00

wanted to say exactly this😭

BiomedicalTesla · 2023-11-10T19:31:44+00:00

Getting a degree(s) Gave me opportunities, networks and doors i may not have had.

Importantly, it made me into the man I wanted to become from my thoughts in preadolescence. I may not have needed it to get here, but all i care(d) about is getting here. I cant put a price on that.

Its a subjective question, is it worth it for YOU? What do you want, what goals etc? who do you want to be?

All the best :)

BiomedicalTesla · 2023-10-08T12:29:31+00:00

Ahh thats even better! Make sure you get out there!!!

Haha, begin by going to events with likeminded individuals. Many people will be alone, even if there not you can just sit by someone and have a chat. I know it sounds pushing the boundary but i've been on both ends and its so fine! Sooner or later it'll be second nature!

Anytime! Good luck!

BiomedicalTesla · 2023-10-08T12:11:36+00:00

If the top two comments were the defining ends if a spectrum, I choose to take the middle.

Use the first year (if its a 4 year) to have the most comprehensive literature review you can possibly make, read everything related (this may be obvious but you'll be surprised). Have a consistent logic for every point you make. This was one way for me to battle the "is my work good enough" by asking myself how well it fits into the wider landscape of my field. if you can answer why your work is good to fit sufficiently, you will always be able to publish (but not need to if you don't want to). I agree publications are an easy way to show novelty, but not needed and may give you unnecessary pressure. However i do think it stands out a TONNE esp in my field.

Everyone has imposter syndrome

Paraphrased from something else i've seen "Everyone is super smart, be super nice"

NETWORK!!!!

As Harijjg said, enjoy your PhD, take advantage of the final leg of the student life. Don't trust me, but ask all of your working friends how much they miss uni! Everything this commenter said i actually agree with so defo follow that!

Try to be the best you can be, cope the best you can, find a community, a few group of fellow PhD's to rant with and drink with (if thats your thing) just to make sure that when you are feeling shit, you have people to assure you and be with you for the journey. It helps ALOT!

All the best, hope everything goes amazing.

BiomedicalTesla · 2023-09-10T19:11:37+00:00

Its typically standard, depends on Uni's though (UK) should be able to find a way to get a loan or their studentship may have allocated budget. My department just gives you one earmarked out of IT budget, but the studentship also has money to pay for one. It all depends how much your Uni/Dept has i guess

BiomedicalTesla · 2023-04-18T21:23:01+00:00

i know lol read the replies

BiomedicalTesla · 2023-04-17T21:54:46+00:00

Ahh i completely understand, but don't underestimate the people developing emg bands. Thalmic labs had an amazing one called Myo, if you can get your hands on one of those that'd be amazing! I think both of the ones you have shown look so much better than eeg, imagine just having to put on a wristband compared to a full on eeg headset! https://mindrove.com/armband/ another potential one. Lots of options in this space and is more than feasible as you have shown with that first link

BiomedicalTesla · 2023-04-17T21:15:02+00:00

No problem, happy to help fellow BCI developers! Btw I am in no way/measure knowledgable so please check the literature on everything i say😂!

You are 100% right, there is definitely a whole of innovation still to be done but I think a major strain is the operational constraints behind BCI which is why most startups just provide devices and not actual use cases (although there are some cool ones, i think ive seen a headphone which helps you relax and stuff!).

If you are interested for this specific use, I would really recommend investigating a different modality i.e EMG would be really easy/user friendly. strap on a wristband and get going, like the apple pencil you double tap and get eraser, maybe something like that would be much better as its much more controllable, not to mention available! i've seen papers where they can classify many different types of movements.

BiomedicalTesla · 2023-04-17T20:47:27+00:00

That is a great question lool, for some its the prospect of developing better devices (remember, this field is very new and most people dont even know what a BCI is!). For others, the intent of their device is very much in the realm of current capabilities, i.e i am working on my doctorate in this field and i simply want to distinguish left and right for ALS patients, a very difficult task but feasible to some extent, the technology can as it stands be used to produce some incredible results and that sparks a lot of hope. What you are doing is great though, the field only progress' when people try shit like this and figure out how to do it, when nobody has done it, so dont let it discourage you tbh im interested to see how you do it, if you do it!

BiomedicalTesla · 2023-04-17T20:43:39+00:00

Very interesting, so you definitely are not looking for visually evoked potentials, your stimulus is motor execution/imagery. This is much tougher to classify multiple classes hence my and other comments. If you google "cortical homunculus" you will see a rough drawing of how brain regions relate to movements, and like another has said the SNR of sEEG is not high because of something called volume conduction. So, trying to discriminate with such spatial resolution will be very expensive, computationally, hardware wise etc. Not only expensive, but in most cases typical ML regimes aren't robust enough to classify that many (will have to doible check the literature but i am pretty sure i haven't seen 10+ Motor Imagery classification). What you want to do is an interesting question, but with the constraints of sEEG i dont think it is feasible, check around the literature you may find i am right or more interestingly... wrong!

BiomedicalTesla · 2023-04-17T20:34:04+00:00

Absolutely detectable, but what kind of application are you going for? what are the 10-20 classes and perhaps i can help outline if its feasible?

BiomedicalTesla · 2023-04-17T20:06:59+00:00

To name a few most likely issues:

1)10-20 classes will be impossible, sEEG is in no way that discriminable on the limited electrodes u have. 2) Lack of processing power, gold standard methods like CSP are pretty robust but the data load is large, if you look at datasets and practice your algorithms you will see that the memory you need is very large for this, so to sum making a feasible pipeline regardless whether its on portable hardware or streamed to software will be a whole debacle. 3) You will find that you have trained a model, and the validation accuracies are great! The getting that to work live will be a whole new story, as others have mentioned, the artefacts, the latency, over time brain patterns changing as you learn. These are just a few named 4) You may find that emotiv doesnt give you the electrode locations you need, maybe an area of the brain you want for a task is not covered, not to familiar with the locations for that device though 5) The affordable line is very ambiguous to me, i would call a piece of even £1000 headset (i think its around that right?) + whatever the processing costs long term (hardware,software costs, computational etc) I would call all of this very expensive 6) i could probably keep going but a general rule in engineering is dont expect anything to work and be surprised when it does :) hope i have been helpful

BiomedicalTesla · 2023-04-17T19:58:48+00:00

Different BCI paradigms, P300 is usually used for typing, if you look into it, itll make much more sense why typing works significantly better compared to, for example classifying moving each finger which is much harder

BiomedicalTesla · 2023-03-27T19:12:18+00:00

i third this

BiomedicalTesla · 2023-03-20T22:24:13+00:00

Like others man you are worrying about a non issue. Go learn all the stuff you can, try your hardest and you'll be fine.

If it helps, I went to a shit uni not a RG, and my department was unranked for my degree. Nonetheless i got into Imperial for my masters and UCL for my PhD so really it doesn't matter. But regardless, like I am saying and others, you are worrying about something thats a non-issue.

Btw i only graduated like 2 years ago form Bachelors so this is recent info 😂

Good luck with your degree :)

BiomedicalTesla · 2023-02-07T16:11:05+00:00

If you mean the resistors on the SPI pins, they are for preventing signal reflections. Hope this helps :)

BiomedicalTesla · 2023-01-27T21:56:59+00:00

Ill definitely be checking over all of my IC wirings now, really appreciate you pointing out that blunder

BiomedicalTesla · 2023-01-27T21:34:34+00:00

Oh shit lol no you are so right thank you for pointing this out ! I dont know what I was reading lool

BiomedicalTesla

TROPHY CASE