2023 Halloween Hackfest: A Spooky Muscle-Brain Interface

What could be better than a Halloween decoration? Something more perennial, or even something that could also be found in a classroom or lab. Something like [Markus Bindhammer]’s spooky muscle-brain interface. It was inspired by a series called “Tales From the Loop” in which a character’s muscle electrical activity is measured in preparation to adjust his prosthetic hand.

Essentially, it does what you think it does: attach the sensors to your muscles, move them around, and watch the brain light up. [Markus] started with a children’s learning kit that involves molding the brain and discs out of red rubbery goop, the vertebrae out of plaster, and then assembling the whole thing.

Instead, [Markus] molded the brain and vertebrae in two-part silicone for durability, and used two-component colored epoxy for the discs.

As the inspiring series is set in the 80s (we assume the brown, dingy 80s and not the fun, neon 80s), [Markus] gave the enclosure/stand an appropriate color scheme. Inside that box there’s an Arduino Pro Micro, a Grove EMG detector, and a mini step-up converter module. And of course, under the brain, there’s a NeoPixel ring. Don’t miss the build and demo video after the break.

There are a ton of things you can do with blinkenlights for Halloween. How about a light-up candy slide, or a bucket that seems them coming?

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Brain electrodes

Brain Interface Uses Tiny Needles

We often look at news out of the research community and think, “we could build that.” But the latest brain-machine interface from an international team including the Georgia Institute of Technology actually scares us. It uses an array of tiny needles that penetrate the skin but remain too small for your nerves to detect. Right. We assume they need to be sterile but either way, we don’t really want to build a pin grid array to attach to your brain.

It seems the soft device is comfortable and since it is very lightweight it doesn’t suffer from noise if the user blinks or otherwise moves. Looking at the picture of the electrodes, they look awfully pointy, but we assume that’s magnified quite a few times, since the post mentions they are not visible to the naked eye.

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Twitter Brain Interface

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Just in time for the influx of sedentary Oprah viewers, [Adam Wilson] built a brain interface that allows you to post Twitter messages. The electrode cap monitors the user’s brain functions to determine where they’re looking. The display slowly flashes each letter in the alphabet. The user focuses on the letter they want and when it flashes the cap can pick up the resulting impulse. It’s a long process and the average user can only do ten characters a minute i.e. 14 minutes to use all 140 characters in a Twitter post. It’s interesting research and shows how far we still need to go with neural interfaces. The researchers note that Twitter’s forced brevity levels the playing field between locked-in patients and normal users. A video of the device in use is available on the NITRO blog.

Related: KanEye tracking system

[via @johl]

ArdEEG Lowers The Cost Of Brain-Computer Interfaces

Considering the incredible potential offered by brain-computer interfaces (BCIs), it’s no wonder there are so many companies scrambling to make their mark in the field. Some see it as an assistive technology, while others imagine it as the future of interactive entertainment. Regardless of the application, the technology has yet to make much inroads with the DIY crowd — largely due to the complexity and cost of the hardware involved.

But that might change in the near future thanks to projects like ardEEG from [Ildar Rakhmatulin]. This open source shield mounts to the top of the Arduino UNO R4 WiFi and features eight channels for collecting electroencephalogram (EEG) data, such as from a dry electrode cap. The signals can then be processed on the computer using the provided Python example code. From there, the raw data can be visualized or plugged into whatever application you have in mind.

Why target the relatively uncommon WiFi version of the Uno? It’s probably obvious for those with experience with this kind of hardware, but for safety, the system needs complete electrical isolation. The Arduino and shield are powered by a common USB battery bank, and all communication is done over WiFi. Even still, the documentation is clear that the ardEEG is not a medical device, and hasn’t been certified by any regulatory agency — its use is entirely at your own risk.

[Ildar] tells us the hardware will be available soon and should cost under $250, making it one of the most affordable BCI development platforms out there. As with his earlier PiEEG project, the hope is that basing the system around a common device in the hacker and maker scene will help democratize access to BCI research.

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Microelectrode (Utay) array and the decoding of the corresponding actions. (Francis R. Willet et al., 2023)

Restoring A Person’s Voice Using A Brain-Computer Interface

Being able to vocalize is one of the most essential elements of the human experience, with infants expected to start babbling their first words before they’re one year old, and much of their further life revolving around interacting with others using vocalizations involving varying degrees of vocabulary and fluency. This makes the impairment or loss of this ability difficult to devastating, as is the case with locked-in syndrome (LIS), amyotrophic lateral sclerosis (ALS) and similar conditions, where talking and vocalizing has or will become impossible.

In a number of concurrent studies, the use of a brain-computer interface (BCI) is investigated to help patients suffering from LIS (Sean L. Metzger et al., 2023) and ALS (Francis R. Willett et al., 2023) to regain their speaking voice. Using the surgically implanted microelectrode arrays (Utah arrays) electrical impulses pertaining to the patient’s muscles involved in speaking are recorded and mapped to phonemes, which are the elements that make up speech. Each of these phonemes requires a specific configuration of the muscles of the vocal tract (e.g. lips, tongue, jaw and larynx), which can be measured with a fair degree of accuracy. Continue reading “Restoring A Person’s Voice Using A Brain-Computer Interface”

Noninvasive Sensors For Brain–Machine Interfaces Based On Micropatterned Epitaxial Graphene

As fun as brain-computer interfaces (BCI) are, for the best results they tend to come with the major asterisk of requiring the cutting and lifting of a section of the skull in order to implant a Utah array or similar electrode system. A non-invasive alternative consists out of electrodes which are placed on the skin, yet at a reduced resolution. These electrodes are the subject of a recent experiment by [Shaikh Nayeem Faisal] and colleagues in ACS Applied NanoMaterials employing graphene-coated electrodes in an attempt to optimize their performance.

Impedance values of eight-channel FEG and eight-channel HPEG sensor systems placed on the occipital area of the head. (Faisal et al., 2023)
Impedance values of eight-channel FEG and eight-channel HPEG sensor systems placed on the occipital area of the head. (Faisal et al., 2023)

Although external electrodes can be acceptable for basic tasks, such as registering a response to a specific (visual) impulse or for EEG recordings, they can be impractical in general use. Much of this is due to the disadvantages of the ‘wet’ and ‘dry’ varieties, which as the name suggests involve an electrically conductive gel with the former.

This gel ensures solid contact and a resistance of no more than 5 – 30 kΩ at 50 Hz, whereas dry sensors perform rather poorly at >200 kΩ at 50 Hz with worse signal-to-noise characteristics, even before adding in issues such as using the sensor on a hairy scalp, as tends to be the case for most human subjects.

In this study, they created electrode arrays in a number of configurations, each of which used graphene as the interface material. The goal was to get a signal even through human hair — such as on the back of the head near the visual cortex — that would be on-par with wet electrodes. The researchers got very promising results with hex-patterned epitaxial graphene (HEPG) sensors, and even in this early prototype stage, the technique could offer an alternative where wet electrodes are not an option.

While the subject is complex, brain-computer interfaces don’t have to be the sole domain of research laboratories. We recently covered an open hardware Raspberry Pi add-on that can let you experiment with detecting and filtering biosignals from the comfort of your own home.

PiEEG Offers Affordable Brain-Computer Interface

One day in the future, we may interact with our electronic devices not with physical input or even voice commands, but simply by thinking about what we want to do. Such brain–computer interfaces (BCIs), combined with machine learning, could allow us to turn our ideas into reality faster and with less effort than ever before — imagine being able to produce a PCB design simply by thinking about how the completed circuit would work. Of course as an assistive technology, BCIs would be nothing less than life-changing for many.

Today BCIs are in their infancy, but that doesn’t mean there isn’t room for hackers and makers to experiment with the concept. [Ildar Rakhmatulin] has been working on low-cost open source BCIs for years, and with the recent release of his PiEEG on Crowd Supply, thinks he’s finally found an affordable solution that will let individuals experiment with this cutting edge technology.

Implemented as a shield that can be connected to a Raspberry Pi 3 or 4, the PiEEG features 8 channels for connecting wet or dry electrodes that can measure biosignals such as those used in electroencephalography (EEG), electromyography (EMG), and electrocardiography (ECG). With the electrodes connected, reading these biosignals is as easy as running a Python script. While primarily designed for neuroscience experimentation, [Ildar] says the device is also useful for learning more about signal processing, filters, and machine learning.

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