Brain Computer Interface And Its Application In The Medical Field (Research Paper Sample)

Brain-Computer Interface
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Brain-Computer Interface
Introduction
Brain-Computer Interface (BCI) is a dramatically growing and emergent technology that researchers are setting out to build a direct channel between the human brain and the computer (Agrawal, 2013). The integration of the human brain and computer sets out that a brain accepts and control a mechanical device as a regular part of its representation of the whole body. It should be emphasized that the BCI has numerous applications including for the disabled. This following the fact most the applications are correlated to disabled people who can aid them to live as ordinary people. The technology is constituted by a consistent emerging field of research where since the inception of the human race the possibility of performing specific actions based on thoughts has been a prevailing pursuit that will soon materialize with the advent of BCI (Sørensen et al., 2014). The technology has always been geared towards researching mapping, assisting, augmenting, sensory-motor functions and repairing human cognitive. In looking at the origination of BCI, the research began during the early 1970's at the University of California, Los Angeles when it was awarded a grant from the National Science Foundation.
The advent of PCs was a significant innovation in this particular area of research as it redefined the meaning of Brain-Computer Interface (BCI). The computing process of computers has initiated the possibility of analyzing the patterns of electrical signals generated by the human brain thereby enabling the triggered desired outcome instigated by an identified brain signal pattern (Agrawal, 2013). With this revelation, it would be accurate derive that the advent of BCI will have a positive impact on the medical field, games, and entertainment as well as remote communication following the numerous possibilities that were initially thought to be impossible (Ang et al., 2015). According to McCane et al., (2015) the opportunities of various applications of BCI will be extensively beneficial to numerous fields including the Artificial Intelligence as well as computational intelligence. For this reason, this scope sets to explore the major concepts behind BCI following the fact the concepts of basic brain anatomy have been laid out in numerous preliminary research, and the various signals stated.
Brain-Computer Interface Research
The brain is hugely covered with a network of various types of neurons that mark as the process units of information. Most of the neurons are made up of four functionalities in input, conductible, output as well as viz. These neurons go an extra mile towards interpreting data from the synaptic terminals and processing it in the cell body. For this reason, they often fire when the data is more than the verge value set according to the experience. According to Norton et al., (2015) the human brain is reported to be divided into 53 discrete local sections identified as a cytoarchitectural map with each processing different forms of data. In this regards, when the visual data is being processed through the neurons in these sections, the neurons generate electric pulses and signals as well as magnetic fields in carrying out actions through actuators of the body. For this reason, the electric signals in the brain are then synthesized through the pumping of ions such as Sodium, (Na+), Calcium (Ca++) and Potassium (K+), through the neuron membranes in the direction dominated through the membrane potential (Bashford, et al., 2017).
With the above revelation, it would be imperative to explore research on the brain-computer interface following the comprehensive assessment of the human brain functionality. According to Nanda, Sethi & Rout, (n.d), the BCI has numerous phases but is constituted by four primary stages, Signal Acquisition, Signal Pre-Processing, and Signal Classification as well as Computer Interaction. Under the signal acquisition, it is derived that the electric signals synthesized by the neurons are acquired as well as processed through signal acquisition as well as processing devices and techniques. More importantly, there are only two forms of signal acquisition techniques in the brain identified as invasive acquisition and non-invasive acquisition. According to Lew et al., (2014) invasive acquisition techniques are utilized to locate and capture electrophysiological signals through implanted electrodes in the brain tissue directly from the cerebral cortex. However, the invasive signals require surgery for the embedding of electrodes. This is often achieved through the opening of the skull through a definitive surgical procedure known as the craniotomy (Gargava, & Asawa, 2017). Nonetheless, the invasive signal acquisition techniques are reported to provide remarkable quality signals. On the other hand, non-invasive acquisitions are techniques are utilized to identify and capture the signals from the scalp through the use of functional magnetic resonance imaging and electroencephalogram (Lim et al., 2017). Following the fact this technique doesn’t require surgery to implant the electrodes, electroencephalogram is the most widely used form of non-invasive brain-computer interfaces. Moreover, it is advantageous for its simplicity as well as ease of utilization which further meets the specification for BCI when it comes to practical use.
Signal Pre-Processing
Naros, & Gharabaghi (2015) derives that the despite the technique used, the unwanted noise signal is always inevitable. More significantly, the EEG recording is not only constituted by electrical brain signals but other unwanted signals such as EMG signals instigated by muscular activity and electronic equipment interference including power supply signals. Therefore, to ensure that the signals captured are not biased, various processing techniques to omit the unwanted signals. Among these techniques is the Basic Filtering which is the process of removing power supply signals through a band filter. This filter passes most of the frequencies unaltered and decreases the signals in a specific range to significantly low levels (Lim et al., 2017).
The next process is adaptive filtering where the unfamiliarity of the spectrum of artifacts is taken into account, and the spectrum of the recorded EEG is adapted to reduce the recorded EEG in frequency ranges that often contains the objects (Lew et al., 2014). Finally, the blind source separation process is an alternative approach that is guided by the assumption that EEG signals are excellent approximation through a sequence of sources located in the brain (Bashford et al., 2017). For this reason, in getting rid of artifacts it would be accurate to make the assumption that artifacts are synthesized through a subset of the extracted sources where one removes them and reconstructs the EEG from the remaining clean sources.
Signal Classification
In this phase, it is revealed that it is impossible to categorize the waves as the EEG signals clearly are consistently caught by capturing devices with multiple electrodes that and pick signals in large quantities simultaneously. Therefore, there are six types of crucial signals known as Beta waves, Alpha waves, Theta waves, Delta waves, Gamma waves, and Mu waves (Chu, 2017). After the signals have been cleaned, they are processed and categorized through these signal waves to identify the type of mental function that the individual or subjects are executing.
Computer Interaction
The computational interaction marks as the final phase of the brain-computer interface. Therefore, after the signals have been successfully categorized are utilized by a relevant algorithm for the advancement of a particular application.
BCI Applications
Medical Application
The field of health contains numerous forms of application that could benefit from brain signal related to various therapeutic phases such as prevention, detection, diagnosis, rehabilitation, and restoration. Under prevention, there are factors like smoking, alcoholism and motion sickness while detection and diagnosis comprise of health complexities such as tumors, brain disorders and sleep disorders (Carelli et al., 2017). Finally, rehabilitation and restoration comprise of health issues such as brain stroke, disability, and psychological disorders. However, the application of BCI into medical phases is dependent on the type of signal acquisition utilized, between invasive and non-invasive acquisitions.
Invasive
As established earlier, invasive techniques are used to capture the electrophysiological signals through electrodes that are directly implanted in the brain tissue particularly into the cerebral cortex. The approach can be used in the prevention phase following the influences on attentiveness instigated by alcohol and smoking on brain waves highlighted by numerous studies. Medical prevention is dependent on the loss of function as well as the decrease of alertness level associated with tobacco and abuse of alcohol (Naros, & Gharabaghi, 2015). For enhancing the consciousness levels to prevent motion sickness which is influenced by the use of Alcohol and smoking leads to dramatic rates of road accidents related deaths following the decreased levels of alertness studies have proposed various applications of BCI. Among these is the use of a virtual reality-based motion sickness platform that is designed with a 32 channel EEG system utilized to assess and record motion sickness levels (Guger, et al., 2015). Nonetheless, for the invasive technique to be used in any of the medical phases, the subjects have to undergo surgery to implant the electrodes. These sensors are often implanted through opening the skull through a surgical procedure known as a craniotomy.
Once the electrodes are placed on the subject’s cerebral cortex, they...