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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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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A Sleep Monitor For Minimum Outlay

There are a variety of instruments used in sleep studies to measure bodily activity during sleep and consequent sleep quality. Many of them use techniques that perhaps aren’t so easy to replicate on the bench, but an EEG or electroencephalograph to measure brain waves can be achieved using a readily-available module. [Ben Jabituya] shows us a sleep monitor using one of these modules, an EGG Mikroe Click.

The brains of the operation is an Adafruit Adalogger Feather M0, which is hooked up to a headband containing the sensing electrodes. The write-up gives us a round-up of the available boards, which should be handy for any experimenters in this field. The firmware meanwhile was written using the Arduino IDE. It collects raw sampling data to an SD card, and one surprise comes in just how relatively small a space it requires to store a night’s results.

Finally, a Python script was used to process the data and turn it into a spectrogram to look at brain activity through the night. He envisages using the device for triggering lucid dreaming during REM sleep, but we can see it might be rather useful for sleep disorder sufferers, too. Take a look at it in the video below the break. Continue reading “A Sleep Monitor For Minimum Outlay”

A styrofoam head wearing an electronic headband with cat ears

These Mind-Controlled Cat Ears Move With Your Mood

As any cat owner will tell you, a cat’s ears are great indicators of its state of mind: pointed forward if they want your attention, turned backwards if they’re angry, and folded down flat when they’re afraid. Humans sometimes don cat ear headbands as a fashion statement, but sitting motionless those ears are more likely to confuse a cat than to provide any meaningful communication.

[Jazz DiMauro] aims to fill that gap by designing a cat ear headband that actually responds to your emotions. It does so by continuously taking an EEG measurement and extracting the “attention” and “meditation” variables from it. Those values are then applied to a set of servos that allow two-axis motion on each 3D printed ear. The EEG readout device is an off-the-shelf MindWave headset, which outputs its sensor data through Bluetooth. An Arduino then reads out the data and drives the servos.

Turning all this into a usable wearable device was a project on its own: [Jazz] went through several iterations to find a suitable power source and wiring strategy until they settled on a pair of lithium-polymer batteries and a single flat cable. The end result looks comfortable enough to wear, and the ears’ motion looks smooth and natural. All that’s left is to test it with real cats, to find out if they can now finally understand their human’s emotions too.

We’ve featured a few moving cat ear headbands before: one that moves along with your head’s motions, and another one with manual control. Today’s EEG-powered one shows yet another application for EEGs, which have been used for anything from invoking lucid dreaming to playing beer pong. Continue reading “These Mind-Controlled Cat Ears Move With Your Mood”

The Real Science (Not Armchair Science) Of Consciousness

Among brain researchers there’s a truism that says the reason people underestimate how much unconscious processing goes on in your brain is because you’re not conscious of it. And while there is a lot of unconscious processing, the truism also points out a duality: your brain does both processing that leads to consciousness and processing that does not. As you’ll see below, this duality has opened up a scientific approach to studying consciousness.

Are Subjective Results Scientific?

Researcher checking fMRI images.
Checking fMRI images.

In science we’re used to empirical test results, measurements made in a way that are verifiable, a reading from a calibrated meter where that reading can be made again and again by different people. But what if all you have to go on is what a person says they are experiencing, a subjective observation? That doesn’t sound very scientific.

That lack of non-subjective evidence is a big part of what stalled scientific research into consciousness for many years. But consciousness is unique. While we have measuring tools for observing brain activity, how do you know whether that activity is contributing to a conscious experience or is unconscious? The only way is to ask the person whose brain you’re measuring. Are they conscious of an image being presented to them? If not, then it’s being processed unconsciously. You have to ask them, and their response is, naturally, subjective.

Skepticism about subjective results along with a lack of tools, held back scientific research into consciousness for many years. It was taboo to even use the C-word until the 1980s when researchers decided that subjective results were okay. Since then, here’s been a great deal of scientific research into consciousness and this then is a sampling of that research. And as you’ll see, it’s even saved a life or two.

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Magic In VR That Depends On Your Actual State Of Mind

[Cangar]’s excitement is palpable in his release of a working brain-computer interface (BCI) mod for Skyrim VR, in which the magic system in the game is modified so that spell effectiveness is significantly boosted when the player is in a focused mental state. [Cangar] isn’t just messing around, either. He’s a neuroscientist whose research focuses on assessing mental states during task performance. Luckily for us, he’s also an enthusiastic VR gamer, and this project of his has several interesting aspects that he’s happy to show off in a couple of videos.

User wearing VR headset
The Muse 2 fits under the VR headset easily.

It all starts with the player wearing a Muse 2 meditation device; a type of passive, off-the-shelf electroencephalography (EEG) unit aimed primarily at guiding a user towards better relaxation and focus. [Cangar] reads data using the Brainflow library and processes it into a final value on a scale between “not focused” and “focused”. [Cangar] makes a point of explaining that his system ultimately has the goal of modeling the player’s state of mind, which is different from modeling just the brain activity. As such, motion data is considered as well, and holding still confers a small bonus to the process.

How is this data actually used in the game? In VR, this “focus” value is shown as a small bar on the player’s wrist, and spell effectiveness (for example, damage for attack spells) scales along with the size of the bar. When the bar is full a player would be very powerful, with spells doing double damage. If the bar is empty, spells will do little to no damage.

[Cangar] demonstrates the mod in two videos (both embedded below), but you won’t see him blasting enemies with fireballs. Presumably, VR gamers already know what that looks like, so what he does instead is explain how the system looks and works (first video, cued to 4:12), and in the second, he video demonstrates how the focus meter changes depending on his activity and mental state.

The results look exciting, and the potential uses of a system like this are pretty interesting to think about. Taking a few deep breaths and calming one’s body and mind before launching a magical attack will have a tangible effect in the world, and because things rarely go according to plan, there is also a clear survival benefit to learning to focus while under pressure. But if a brain monitor isn’t your cup of tea, maybe consider a leisurely bike ride through Skyrim, instead.

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Universal Bio-Electrical Signal Amplifier Makes Reading Body Signals Easy

The electrical signals emitted by the human body tell us a lot about what’s going on inside. But getting those signals inside your microcontroller is not straightforward: the voltages are too small for most ADCs, and the ever-present 50 or 60 Hz mains frequency makes it hard to discern subtle changes. Over at Upside Down Labs, [Deepak Kathri] developed a universal biosensor interface called the BioAmp EXG Pill to make all this a lot easier.

Its name refers to the fact that it can be used for several different bio-electrical sensing applications: ECG, EMG, EOG and EEG, which deal with signals coming from the heart, muscles, eyes and brain, respectively. To enable such flexibility, the board has connectors for two or three electrodes, as well as solder pads to mount resistors and capacitors to adjust the gain and bandwidth. An instrumentation amplifier increases the strength of the desired signal while rejecting noise and interference.

The form factor allows easy connection to electrodes on one side and a data acquisition system on the other. Measuring just 25.4 mm long and 10 mm wide, it should be easy to integrate into any type of biosensing gizmo you can come up with. [Deepak] has made several demo setups, showing him using the Pill with an Arduino to measure his heart rate, detect eye blinks, and even control a robot arm using his own arm muscles!

The EXG Pill is an evolution of an earlier EMG-only project. We’ve seen several great ECG and EEG projects before, but is the first time we’ve seen one amplifier that can do them all.