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Brain-Computer Interface: The ultimate guide

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Nishit Agarwal
Brain-Computer Interface: The ultimate guide

Neuralink's new video of a monkey playing Pong with his mind made headlines in early April 2021. People with disabilities will regain their freedom of movement, according to the company's always-bold statements. We decided to go beyond the hype and determine what these brain-computer systems are truly capable of. Let's get started right away.

 

What Exactly is a Brain-Computer Interface (BCI)?

 

Brain-computer interfaces (BCIs) or brain-machine interfaces (BMIs) record and translate a user's brain activity into commands for an external application. Even though the terms are interchangeable, BCI employs externally recorded signals (e.g., electroencephalography)

 

While BMI collects signals from implanted sources, we use the term BCI in a broader sense, implying that both the brain and the system are on the same page in terms of interactive, adaptive control, which is critical for successful BCI.

 

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What Are the Applications of BCI?

 

Initially, the goal of BCI development was to assist paralyzed patients in controlling assistive devices with their thoughts. However, it is also critical for the rehabilitation devices used by stroke patients.

 

What are the Various Types of BCIs?

 

Brain-computer interfaces are classified into three major categories based on the technique used to measure the brain's signal:

 

● Noninvasive

● Semi Invasive

● Invasive

 

In invasive techniques, unique circumstances have to be used to collect data (brain signals), these equipment are inserted directly into the human brain by a crucial surgery. Semi-invasive procedures involve inserting devices into the skull on the top of the human brain.

 

Noninvasive devices are generally thought to be the safest and least expensive type of device. However, due to the obstruction of the skull, these devices can only capture "weaker" human brain signals. So instead, the brain signals are detected using electrodes placed on the scalp.

 

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There are several methods for developing a noninvasive brain-computer interface, including EEG (electroencephalography), MEG (magnetoencephalography), and MRT (magnetic resonance therapy) (magnetic resonance tomography). However, the most commonly used type of BCI for studying is an EEG-based brain-computer interface.

 

EEG signals are processed and decoded into control signals that a computer or robotic device can easily perceive. One of the most difficult aspects of developing a high-quality BCI is the processing and decoding operation. This task is so difficult that science institutions and variation software companies hold competitions from time to time to create EEG signal classification for BCI.

 

What Types of Brain Signals Does BCI Collect?

 

The system can use any brain's electrical signals measured by applications on the scalp, cortical surface, or cortex to control external applications. In a formal sense, the most studied signals are:

 

An intracortical electrode array, electrocorticography (ECoG), electroencephalography (EEG), and magnetoencephalography (MEG) techniques capture electrical and magnetic signals of brain activity.

 

Metabolic signals derived from functional magnetic resonance imaging (fMRI) or functional near-infrared imaging (fNIRS) techniques that measure blood flow in the brain

 

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How Do You Get Started Learning About BCI from scratch?

 

According to Hosea Siu, a Ph.D. student in aerospace engineering, "for direct "brain" interfaces, you need to get a set of EEG electrodes, and then for peripheral nervous system interfaces, you require EMG electrodes."

 

You'll have to do some signal conditioning once you've gotten that data into your computer. Sorting for the frequency of the transmitter you're looking for, for example, or filtering out environmental noise.

 

Following that, you must consider what you want the system to do. For example, is it required to detect a specific change in your EEG patterns when you think about blue? Or do you require it to detect a transformation in your EMG when you move your finger? So, how about the computer? Should it execute a program? Enter some text?

 

Consider how you intend to label your data. For example, how will the computer know that a particular signal is meaningful at first?

 

This is known as supervised learning. First, select your preferred classification method, collect a large amount of labeled data, and train your system. Then, you can use methods like cross-validation to see if your trained models are performing as expected. After all of this, you may have something resembling a brain-computer interface.

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