AI-Driven Brain-Computer Interface (BCI) Unlocking the Minds Potential

Imagine steering a game or selecting a letter with nothing but a blink or a glance. We set out to make that feel normal, not magical, by building a non-invasive brain–computer interface that runs entirely on a low-power microcontroller and fits into everyday wearables like glasses. No surgery, no cloud dependency—just smart sensing, tight signal processing, and a tiny neural net that turns eye movements into reliable commands.

We start with the “why”: millions live with motor impairments yet can still move their eyes, leaving a powerful window for communication and control. From there, we map the BCI landscape—high-precision invasive implants like Neuralink, BrainGate, and Synchron on one side; accessible non-invasive tools like Emotiv, Muse, and OpenBCI on the other—and unpack the trade-offs across accuracy, latency, cost, and ethics. Our approach uses electrostatic charge sensing to read subtle changes around the eyes, with electrodes positioned for comfort and signal quality. A lean pipeline cleans the data with high-pass, notch, and low-pass filters; a Z-score event detector wakes the model only when something meaningful happens.

The model is a compact 1D CNN that classifies four classes—discard involuntary blinks, trigger with a voluntary blink, and detect left or right glances—achieving about 90% accuracy on a small multi-participant dataset. Running on an STM32H7, it uses roughly 18 KB flash and 6 KB RAM, with sub-millisecond inference; the overall response is driven by the short data window at 240 Hz, delivering real-time control for basic tasks. We demo blink-to-jump and look-to-steer gameplay to prove responsiveness and highlight how the same system could power communication aids and smart-home control. Looking ahead, we focus on integrating the electrodes into comfortable glasses, adding quick calibration for personal variability, and expanding the command set without sacrificing simplicity.

If this mix of accessibility, edge AI, and practical human–machine interaction resonates with you, follow the show, share it with a friend, and leave a review so we can reach more builders and caregivers working on assistive tech. What would you control first with a glance?

Transcript

Assistive Roots And Patient Needs

Invasive Vs Non-Invasive Landscape

Closing The Real-Time Gap On Edge

Hardware Stack And Sensor Principle

Electrode Placement And Dataset

Signal Pipeline And Filtering

Event Detection And Windowing

Tiny CNN, Latency, And Accuracy

Live Demo And Next Steps

SPEAKER_00 0:08

Start with a question. Who of you has ever wished to move an object through your thoughts? Imagine you are sitting on a couch and you want your remote control to come to you. So this would be very nice. And so imagine if we could control the world around us with our brain waves. This is the promise of brain computer interface that wants to make bridges between our mind and our external world. Now the example I did might seem fun, fancy, futuristic for most of us, but there are part of the population for whom this technology could be life-changing. In the world, we have many people affected by neurological conditions that have uh heavily that are heavily that experience uh heavily motor dysfunctions. So consider, for example, uh, people affected by multiple sclerosis. And globally we have more than 2.8 million of individuals suffering of this disease. And yet most of them retain the ability to move their eyes. So we have this small window of control. We can use these eye movements to enable them to restore the communication between their mind and the external world. And that's why brain computer interface at the beginning started with assistive applications. Now it's evolving also with gaming applications and much more. But the the very first purpose was assisting people. And obviously, we have many challenges in assisting people because they struggle with interacting with the external world but also with the technology. Because many times the person has to adapt to the system rather than the opposite. And moreover, it's very difficult to grab these signals accurately, to process them in a real-time way efficiently. So there are very many challenges to be faced. We already have commercially many solutions, and we can divide them into main categories. On one side we have invasive solutions, and I'm quite sure that many of you already know the Neuralink project by Elon Musk, but there are also brain gate, synchrone, and on the other side we have non-invasive techniques such as emotive, neurosky, muse, open BCI. On one side, the invasive techniques require a surgical procedure to install these devices into your brain. So you can imagine that they are not for everyone, that are designed for specific use cases, and they are very expensive, so they cannot be uh they are not affordable for everyone. But they are very precise, they have high accuracy, and they can be used for complex tasks such as control a robotic arm in real time. And uh but they pose also some ethical uh questions, because Elon Musk, for example, wants to use Neuralink in a first uh phase just for medical application, but then his uh purpose is to uh cognitive augment our brain. So let's talk about this. Maybe we have some medical concerns. And uh on the other side we have non-invasive uh techniques, and uh obviously they are much less accurate than the invasive systems, they are not working in real time, but they are cheap, uh, they have uh they are very easy to uh to use. And uh with this work that I'm going to present you, we want somehow to fill this gap because we would like to have a real-time system working on a cheap uh on a cheap device. And uh this is our aim. And uh for the time being, we are working on non-invasive system and uh we are focusing on a gaming application, but our aim is to possibly exploit it uh on a system device. These are the main limitations of uh non-invasive uh BCI systems up to now. Uh most of the processing is happening outside the board. So you have very limited uh processing happening on board, most of it is happening on a PC or on the cloud. And uh moreover, they have very limited real-time performances and they target very narrow applications and that are related to mindfulness, meditations, they analyze the brain waves. We started our uh our approach uh with uh we started our technique at the beginning using the helmet by from OpenBCI, and I can assure you that it is quite uncomfortable because it has some pins that uh are in your forehead, so it's not so comfort. And uh but now we are switching uh to use uh our biosensor developed by ST together with an STM32 microcontroller where the whole processing happens. So the advantage uh of this system with respect to what is available in the market is that it runs completely on an edge device, so and it runs in real time, as I will show you later. But let's go into the details of the system. So our uh our project is composed by a biosensor developed by ST, then a microcontroller STM32H7 that runs the whole uh process, and then the final command is sent to APC for a gaming application that I will show you later on uh on a video. But what's the principle of uh the biosensor? It is based on the electrostatic uh charge sensing principle. So when we have an object, it is usually uh neutral from the electrostatic standpoint uh because uh its number of uh electrons equals its number of protons. But when we they touch and then separate, there are the electrons that uh that move from one uh object to another one. And this is uh these are electrostatic charge that create fluctuations in the electric field. So this sensor, this biosensor, measures this change, uh this change, and it uses electrodes to feel this change in the electric field and an electronic circuit to amplify and process the signal. How we used these uh these electrons, okay. This is one of my colleagues that volunteered for this task, and uh we exploited the different configuration for the electrodes, and uh but uh we decided to uh to use the eye movements because, as I already said, many people retain the possibility to move the eyes. And also we uh decided to use this configuration because we could exploit it in wearable glasses. Uh because we exploited also other configurations that could be suitable for visors. And uh sometimes they are most effective. But uh at uh the at this time we are going uh we are targeting uh wearable glasses, so we decided to put the electrodes on the nose and uh uh the electrodes in uh behind the here is as a reference. We collected the data set using uh with nine people and we decided to grab four different classes. The first one is unvoluntary blink, but because just because we want to discard it, because we don't want to control anything with our involuntary action. Then there is the voluntary blink that can be used like a click of the mouse. And then we have uh looking right and looking left. I mean we are looking to the center, then we look right and come back without moving the head. This is quite challenging, and then look left and come back. And then we designed a completely software pipeline. We started obviously designing the whole system on the PC, and then we deployed it on the firmware on our microcontroller. And uh this uh pipeline is composed by different blocks that I'm going to explain in details. The first block is a pre-processing one. Then we have in parallel a data collection block and an event detection, and finally we have an event classification that is based on a one-dimensional convolutional neural network. The final class that is recognized is sent via Usart to the PC for controlling the game. So why the pre-processing? Because data are quite noisy, and uh uh we are using uh three different uh filters for pre-processing the signal. First of all, we are using an high pass filter to remove the DC component, then we are using a notch filter to remove the frequencies around 50 Hz to remove the power supply's frequency, and finally we are using a low pass filter with a cutoff frequency of 2 Hz for removing extra noise that is uh in uh uh in uh harmonic at higher frequencies. And here there is a simple of raw signals on the left uh before any processing, any pre-processing. And on the right, you can see the effect of applying these filters to the signals. So you can see that uh the uh the signal is much is centered around the zero, and uh moreover it's uh much uh less affected by noise. And this is helpful uh to have also a small neural network after, because uh the neural network doesn't have to learn the noise, so can learn directly the uh the cleaned signal. After this uh this pass, we have an event detection. We could have uh used directly a temporal uh convolution network for processing directly uh the signal, but uh it would have been uh always on. We decided to have a very simple algorithm, always on, that is this event detection, and then it activates the neural network for the processing only when there is a relevant signal. And this uh event detector is based on Z score, it is a well-known solution. We didn't invent anything here. Uh and uh Z score is uh tells you how far is your signal from the average uh in terms of standard deviations. And uh consider that in this example uh you uh okay, maybe it is visible, okay. Here, uh the event detection doesn't recognize perfectly the peak. Uh because where is the point? Okay, the peak. It recognizes, for example, this part. This is because it is working in real time. So we don't have the whole the whole signal. Uh in this moment we have just this part of the signal. So the Z score doesn't know that uh the signal is going to grow. But this is sufficient to understand that something uh is happening, something different that uh than the normal behavior. So when we detect uh this uh event, we collect uh one uh 100 samples before and 200 samples before for a total of 300 samples. And uh consider that in the firmware implementation, these 100 samples are already grabbed because we have implemented a FIFO mechanism. Okay, after that we have a very, very simple neural network. It is composed by very common layers, convolution, max pooling, uh RELU, softmax. And as you can see, its uh input is uh 300 because it is the size of our window, uh by two uh because we have two channels. And the results we gained on this limited uh dataset are quite promising because we obtained about the 90% of accuracy using a K-fold cross-validation procedure, and the flash is just uh 18 kilobytes, the RAM six kilobytes. And the more interesting part is that this CNN runs in just 0.76 milliseconds. But we need much more time to grab the signal because we have 300 samples at 240 Hertz, that means 1.25 seconds to collect the window and 0.75 milliseconds to execute the network. So it is real time. And now I can show you the demo. Okay, so here we have again uh Vincenzo, my colleague, that is uh wearing these electrodes, and uh he will uh show he will command this uh video game. So when he looks uh here you can see the commands that is identified. When he voluntary blink, the car uh jumps to avoid the obstacle, and when when he look uh right the the car moves right, hopefully if it is correctly understood. And the purpose is uh to get the stick coins along the way. So let's see. Okay, he did uh the score here is incrementing. Okay, so this was just a fun uh application, but to understand if the system is working. Now our aim is to embed this system in wearable glasses and to make uh real-time testing to receive also feedback. Uh, because uh we saw also that uh brain waves and also electrooculography can vary from one person to another. So we need to explore also some system of personalization. So maybe a calibration procedure will be needed to adjust the weights of the network. But uh yes, we are in this process of exploring more, and yes, we would like also to expand the functionalities. Thank you.