Tuesday, 28 January 2020

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Everything you need to know about Brain-Computer Interface

                                                                                 Brain Computing Interface


Brain-Computer Interface (BCI): devices that enable its users to interact with computers by mean of brain-activity only, this activity being generally measured by ElectroEncephaloGraphy (EEG).

Electroencephalography (EEG): physiological method of choice to record the electrical activity generated by the brain via electrodes placed on the scalp surface.

The research on BCIs started at the University of California, which led to the emergence of the expression brain–computer interface. The focus of BCI research and development continues to be primarily on neuroprosthetics applications that can help restore damaged sight, hearing, and movement. The mid-1990s marked the appearance of the first neuroprosthetic devices for humans. BCI doesn’t read the mind accurately, but detects the smallest of changes in the energy radiated by the brain when you think in a certain way. A BCI recognizes specific energy/ frequency patterns in the brain.
There are currently two main technologies, fMRI and EEG. The first requires a massive machine, but the second, with consumer headsets like Emotiv and Neurosky, has actually become available to a more general audience.” However, BCI can also be a promising interaction tool for healthy people, with several potential applications in the field of multimedia, VR or video games among many other potential applications. Davide Valeriani said that “The EEG hardware is totally safe for the user, but records very noisy signals. Also, research labs have been mainly focused on using it to understand the brain and to propose innovative applications without any follow-up in commercial products.

 How Brain works

 In simple terms, your brain is divided into two main sections: 

The limbic system 
The neocortex.

The limbic system is responsible for our primal urges, as well as those related to survival, such as eating and reproducing. Our neocortex is the most advanced area, and it’s responsible for logical functions that make us good at languages, technology, business, and philosophy. The human brains contains about 86 bilion nerve cells called neurons, each individually linked to other neurons by way of connectors called axons and dendrites. Every time, we think, move or feel, neurons are at work. Indeed, the brain generates huge amount of neural activities. Basically, small electric signals that moves from neuron to neuron are doing the work. There are many signals that can be used for BCI.

 These signals can be divided into two categories: - Spikes - Field potentials

According to Boris Reuderink, Machine Learning Consultant at Cortext, “One of the bigger problems in brain-computer interfaces is that the brain signals are weak and very variable. This is why it is difficult to train a classifier, and use it the next day, let alone use it on a different subject.

 Let’s try to elaborate on these aspects a bit more below. Each of these aspects  have their own field of research. Signal Production There are two ways of producing these brain signals

Signal Production There are two ways of producing these brain signals: 
 advantage that signal detection is easier, since you have control over the stimuli; you know for example when they are presented. This is harder in the case where you are just reading brain-waves from the subject.”                                         
 Signal Detection  There are different ways to detect brain signals. The most well known are EEG and fMRI, but there are others as well. EEG measures the electrical activity of the brain, fMRI the blood-flow in the brain. Each of these methods have their own dis/advantages. Some have a better temporal resolution (they can detect brain-activity as it happens), while others have a better spatial resolution (they can pin-point the location of activity). The idea remains largely the same for other types of measuring techniques.   
Signal Processing : One of the issues we will find when dealing with brain-data, is that the data tends to contain a lot of noise. When using EEG, for example, things like grinding of the teeth will show in the data, as well as eye-movements. This noise needs to be filtered out. The data can now be used for detecting actual signals. When the subject is actively generating signals , we are usually aware of the kind of signals we want to detect. One example is the P300 wave, which is a so-called event related potential that will show up when an infrequent, task-relevant stimulus is presented. This wave will show up as a large peak in your data and you might try different techniques from machine learning to detect such peaks. Signal Transduction When you have detected the interesting signals in your data, you want to use them in some way that is helpful to someone. The subject could for example use the BCI to control a mouse by means of imagined movement. One problem you will encounter here is that you need to use the data you receive from the subject as efficiently as possible, while at the same time you have to keep in mind that BCI’s can make mistakes. Current BCI’s are relatively slow and make mistakes once in a while (For instance, the computer thinks you imagined left-hand movement, while in fact you imagined right-hand movement).”
                                                                                                                           
Signal Transduction When you have detected the interesting signals in your data, you want to use them in some way that is helpful to someone. The subject could for example use the BCI to control a mouse by means of imagined movement. One problem you will encounter here is that you need to use the data you receive from the subject as efficiently as possible, while at the same time you have to keep in mind that BCI’s can make mistakes. Current BCI’s are relatively slow and make mistakes once in a while (For instance, the computer thinks you imagined left-hand movement, while in fact you imagined right-hand movement).”
                                                                                                                               
There are several ways to develop a noninvasive brain-computer interface, such as EEG (electroencephalography), MEG (magnetoencephalography), or MRT (magnetic resonance tomography). An EEG-based brain-computer interface is the most preferred type of BCI for studying. EEG signals are processed and decoded in control signals, which a computer or a robotic device perceives readily. The processing and decoding operation is one of the most complicated phases of building a good-quality BCI. In particular, this task is so difficult that from time to time science institutions and various software companies organize competitions to create EEG signals.   
                           
BCI from Scratch : you need a set of EEG electrodes, and for peripheral nervous system interfaces, you need EMG electrodes. Once you can get that data into your computer, you’ll need to do some signal conditioning. Things like filtering for the frequency of signal you’re looking for, filtering out environmental noise (60 Hz noise from electrical lines is common in the US…). After, you need to think about what you’re actually trying to have the system do. Do you need it to detect a particular change in your EEG patterns when you think about the color blue? Or do you need it to detect a change in your EMG when you’re moving a finger? What about the computer? Should it run a program? Type some text? Think about how you’re going to label your data. How will the computer know initially that a particular signal is meaningful? This is supervised learning. Choose your preferred classification method, get lots of labeled data, and train your system. You can use methods like cross-validation to check if your trained models are doing what you think they’re supposed to. After all of this, you might have something that looks like a brain-computer interface.”                                      

You can find several publicly available EEG datasets in the following website:


http://bbci.de/

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