Have you ever thought about a device that can be operated using your thoughts? Can people communicate with their thoughts rather than using any other verbal or non-verbal communication? Indeed, it is possible with the help of Artificial Intelligence-based Brain-computer Interface (BCI) systems.

BCI translates the brain activity into signals which software can understand. With the help of BCI, we can measure our brain’s activity and use it to control a remote device. Isn’t that thrilling?

In this article, we will help you know more about BCI and how it works to perform such a fantastic task. Let us get into the core of BCI. 

What are BCI systems?

Before we go deep into the subject, let’s start with knowing the basics and importance of BCI systems.

BCI systems are an integrated form of hardware and software that can control external devices through brain activity. Accordingly, brain activity is measured from different brain regions associated with thoughts.

Importance of BCI

BCI allows people to control machines using their thoughts. These interfaces can help people with disabilities.

It has the potential to improve the lives of people with neuromuscular disabilities. BCI also enhances human-computer interactions. It helps communication between the brain and other external devices.

How Does BCI Work?

Now that you know, BCI is an artificial intelligence (AI) system that can identify patterns within the brain signals.

BCI helps measure brain activity and takes out the features from that activity to convert them into outputs. It is useful for replacing, restoring, enhancing, and supplementing human functions.

Four Different Phases of the BCI System

Have you ever wondered how the BCI systems make the interaction smooth and flawless? As we know, BCIs are devices that can connect our brains to computers directly. But how does it improve the quality of our everyday life?

Here are the four successive phases of these AI-backed BCI systems that translate brain signals. Let’s check it out!

a) Signal Production

The first step requires brain signals. 

Typically, there are two ways of producing these brain signals:

• Generate signals when subjects imagine an action (Indirect-speech)
• Generate signals when subjects imagine a speech (Direct-speech)

BCIs are divided into Indirect-speech BCI and Direct-speech BCI, depending on the type of signals handled. The Indirect-speech BCI system utilizes motor imagery signals to translate speech. It provides another means of communication.

However, database preparation using the signals is complicated and time-consuming due to imagined actions or external stimuli.

The Direct-speech BCI system uses imagined and vocalized speech signals. It is more of a natural communication process with a high data transfer rate, unlike the Indirect-speech BCI system.

b) Signal Detection

There are different sensors used to detect brain signals. BCIs are 

• Non-invasive and 
• Invasive

Both types differ depending on the sensor used to measure brain activity.

Non-invasive BCI

Non-invasive BCI does not require direct contact with the brain and uses external sensors to identify neural signals. Therefore, it is easy to use and less risky while minimizing the cost.

Even though there are benefits, non-invasive BCI comes with lower signal resolution. As a result, it may lead to less accurate communication between the brain and the interface.

Invasive BCI 

Moving on to invasive BCI, which requires the surgical insertion of electrodes into the brain, it offers the possibility of capturing high-resolution neural signals.

Invasive BCI allows for more accurate communication between the brain and external devices. Even so, with its numerous benefits, invasive BCI technology has challenges. The major disadvantages include the risk of infection and the complexity of surgical implantation.

Non-invasive and Invasive Brain Sensors

Now that we have discussed the brain sensors, let’s familiarize ourselves with the names of common brain sensors used.

Some non-invasive brain sensors include EEG Electroencephalography, MEG magnetoencephalography, fMRI functional magnetic resonance imaging, fNIRS functional near-infrared spectroscopy, and PET positron emission tomography.

Invasive brain sensors include ECoG Electrocorticography (ECoG) and MEA Microelectrode Array (MEA).

c) Signal Processing

Do you know the most common problem faced with brain data? One of the issues with brain data is that it tends to contain a lot of noise, including power line noise and artifacts.

Hence, this noise needs to be filtered out. 

Therefore, it is necessary to pre-process the data in the first place. It is essential to remove the noise and filter for a particular frequency range (delta, theta, alpha, beta, gamma) depending on the application and type of signals handled.

Later, it is possible to extract features from the data using digital signal processing techniques and statistical methods.       

d) Signal Output

The features are given to the machine learning (ML) algorithm to detect or identify the thoughts. Deep neural networks are the relevant ML technologies that perform ML operations and feature extraction.

As a result, the developer doesn’t need to know the signal processing tools to perform the feature extraction. The output can be used to identify the imagined speech or action. Further, it helps control remote devices.

Also Read: Ways To Convert And Retain Enterprise App Users With Personalization

Applications of BCIs

Even though BCI technology is in the early stage of development, its medical and nonmedical applications offer great capabilities.

Medical Applications

BCIs enable people severely paralyzed by amyotrophic lateral sclerosis, brainstem stroke, laryngeal cancer, cerebral palsy, muscular dystrophies, multiple sclerosis, and so on. Using BCIs, they can easily communicate their needs.

For example, it helps them to operate word processing or other computer programs or even control a neuroprosthesis. They are also promising for enhancing functional recovery in people with strokes, brain or spinal cord injuries, Parkinson’s disease, or other neuromuscular disorders. 

Nonmedical Applications

BCI systems have nonmedical applications as well. For instance, it helps enhance human-machine interactions enabling novel experiences. 

Some nonmedical applications of BCI systems include entertainment and gaming, brain-controlled assistive devices, communication and augmented reality control, brain training and neurofeedback.

Also, BCI is used in neuromarketing, where you can perform market research to get better insights about consumer responses towards promotions and advertisements.

It helps companies optimize marketing campaigns and products as per the response from the brain in real time.

Also Read: Machine Learning – How the Internet is Growing a Brain!

Future of BCI Technology

During the next decades, the research and development in BCIs would grow continuously. There will be widespread applications of BCI technology in our everyday life.

Without doubt, BCI applications will start to get refined in the coming years. The accuracy and efficiency of BCI applications will get better as well.

Today, artificial Intelligence continues to rule the world. With its applications, we make the impossible possible. 

At ThinkPalm, we provide end-to-end artificial intelligence services ranging from deep learning, predictive analytics, customer analysis, chatbots, and sentimental analysis to optical character recognition and many more!

Frequently Asked Questions:

What are the common benefits of brain-computer interfaces?

Brain-computer interfaces help people to control devices and machines with the help of their thoughts. As a result, they enable people with disabilities to communicate with others, support them with reading and writing, and send commands to an external device using brain signals. It also facilitates human-computer interactions.

What are the four major components of BCI?

A BCI technology features four components. It includes (i) signal acquisition, (ii) feature extraction, ((iii) feature translation, and iv) device output.

Does Google work on the brain-computer interface?

Google recently announced a cutting-edge technology that allows users to connect their brains to a computer directly. It is designed to offer extraordinary connectivity.

Concluding Lines

BCIs are slowly moving into the mass market. Technological advancements in artificial intelligence will allow us to control all our needs through our thoughts in the next few years.

Thus, companies might use the device to provide biometric security, monitor the employees’ mental level, track attention during work and so forth. It can help with presentations in PowerPoint or Excel files also.

Therefore, it’s time for business leaders to start building a BCI strategy as soon as possible to address the potential benefits of applications.

As we all know, more research is needed to identify its full potential. However, as long as artificial intelligence evolves, there is more scope for new applications for BCI.

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