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Using Machine Learning In Brain-Computer Interfaces

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Neurotech is a cutting-edge field that’s just beginning to come into its stride. With all of the technological advancements in the past decade, the cost of some equipment has decreased allowing more facilities to do research in this area. Neurotech has the potential to help people speak again, use limbs again, and more.

Some of the main drivers of this advancement is machine learning and data analysis. As we get data from hardware, like EEGs or EMGs, we need something powerful enough to process all of that data and give us meaningful results we can use. Machine learning algorithms have improved dramatically over the years, taking less time and resources to train models and produce more accurate results.

When you start to combine two vastly different kinds of technology like neurotech and machine learning, you start to get new solutions to problems people thought were once impossible to solve. That’s where brain-computer interfaces (BCI) come in. There are already many kinds BCI available for a variety of things, like physical therapy and in-hospital treatments.

It can expand much further than these uses, giving us possibilities like controlling remote objects with just our minds or even communicating with each other with thoughts alone. There have already been people who have played real games with just their minds and a computer. The number of applications for BCI will continue to increase as hardware and software go through the next revolution.

For the moment, let’s discuss how neurotech and machine learning complement each other.

How this fits in with machine learning

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Machine learning is a useful tool for making predictions based on existing data. Some of the algorithms can train models incredibly fast when they are used appropriately. This is important within the context of BCI because you are getting gigabytes worth of data in short periods of time that need to be processed and then used to train a model. If your algorithms aren’t efficient, you could end up with a model that takes years to train.

With all of the data you receive from a BCI user, you can find things like whether they are about to have a seizure or if they are thinking about a specific word. Machine learning can take all of the signal data from a BCI and draw accurate conclusions more often than a human can.

It’s similar to training a model to identify cancerous tissue based on millions of images of both malignant and benign cells. Machine learning models can process more data faster and at a lower cost than humans, which give us more accurate guesses at results, treatments, and outcomes. Medicine produces large amounts of personal data every day that could be used to give patients better treatment plans and better preventative care.

Even knowing how much data is generated from our regular healthcare, nothing produces more data than our brains. Being able to process thoughts in a specific context using a BCI gives us insights to more than we’ve ever known about brain signals and how they translate to actions and thoughts. Figuring out which signals correspond to different thoughts will open up new applications for BCI and its corresponding software.

Potential problems that can be solved

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Neurotech is on the path to help millions of people around the world. It’s already seen some success with patients controlling and feeling with robotic limbs with just their minds. There are also many other applications that are currently being developed. These are some examples of what’s currently being worked on, but it doesn’t even begin to scratch the surface of what we can do with BCI and machine learning coupled together.

BCI has the potential to solve problems like paraplegia, seizures, and even some mental illnesses. This kind of technology will change the way we approach healthcare from diagnosis to treatment to preventative care. It also has the potential to solve more than just medical problems. It can also make us more efficient in every part of our lives.

One day you could be driving your car with just your brain or you could make dinner by just thinking about it. It could help answer questions when there has been a crime committed. When you want to know the name of a song you’re thinking about, you could actually just think about the song and get an answer.

A wide variety of fields will change with the introduction of this technology, from banking to agriculture. The way we address security concerns could change to read brain signals to get our unique signature. It can expand to more than brain-computer interfaces for humans. We could eventually develop the technology to work on animals and insects, opening a new world to explore.

The ethics involved

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One thing we cannot overlook in the beginning are the ethics involved with this powerful technology. Having access to someone’s brain data is some of the most sensitive data you could get from a person. We as machine learning engineers, software engineers, and data scientists have to make sure that we aren’t allowing any questionable activity into neurotech.

Pay attention to how this data is being used and stored. Look into any of the disclaimers that users are signing. Since this technology has the power to target individuals in a way that uses their own thinking patterns, we have to build strong guidelines around this field to keep users safe.

Ethics is one of those things that doesn’t come up until something bad happens and we have the chance to prevent that from happening here. It’s important that we know when to draw the line on new developments. It goes back to the point of whether we’re doing something because we should or to see if we can. This is a field where we really have to take this into consideration because of the wide reaching consequences if we let it get out of control. (Think about atomic energy and the scientists who created it)

Working in this field means that you’re making a lot of the foundational decisions right now. We’re currently building the culture for neurotech and neurotechnologists. That means we have to be careful of the little things we let slide right now because they will become the standard for how we do things going forward.

Tools to get started

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To be honest, I got my start in neurotech from hobby projects I worked on at home. You don’t need an entire research lab or medical grade equipment to start making new applications are to develop meaningful contributions to the field. There are several hardware manufactures that sell EEG devices and more.

– OpenBCI: https://openbci.com/

– Emotiv: https://www.emotiv.com/

– Neurosky: http://neurosky.com/

When you get ready to process the data you get from any of these devices, you should consider using Python for any machine learning you need to handle. There aren’t any specific neuroscience packages or algorithms (that I know of), but Python has a number of libraries that make it easier to work with this kind of data.

– Scikit-learn: https://scikit-learn.org/stable/

– TensorFlow: https://www.tensorflow.org/

– Keras: https://keras.io/

There are a number of other libraries that make machine learning on large datasets more manageable depending on your application.

Conclusion

Right now the field of neurotech is wide open. If you can come up with an idea for an application, this is your chance to jump in and be the first to create it. The challenges that come from working with hardware and software to make a functioning project make something at this level of difficulty worth it. The problems that you will face and solve will benefit people who need them the most as long as you keep ethics in mind.

This is a budding industry that will only continue to gain interest and funding as the hardware becomes more affordable. As new applications are discovered and built, we’ll notice changes in industries like medicine, law, and education. The impact of neurotech reaches further than we can see at the moment, so prepare yourself with good ethics and advanced technical knowledge.

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Milecia McGregor
Milecia is a senior software engineer, but she also has a master's degree in mechanical and aerospace engineering and has published research in machine learning and robotics. She started Flipped Coding in 2017 to help people learn web development with real-world projects. Milecia also speaks at tech conferences around the world. In her free time, she spends time with her husband and dogs while learning kung fu and learning how to play the harmonica.

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