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Brain-computer Interface (BCI), refers to advise that aids in the translation of neuronal information into commands that can control external hardware as well as the software which includes robotic. The unique capabilities of allowing the bidirectional flow of information makes BCI different from neuromodulation. The aim of the interface is to enable direct communication between the controlled object and the brain. Other terms that are used to refer to BCI include; neural control interface (NCI) Direct neural interface (DNI), brain-machine interface (BMI) as well as mind-machine interface (MMI).
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History of Brain-Computer Interface (BCI)
The history of BCI can be recorded from the experiments of Hans Berger when he discovered electrical activities in the human brains as well as the development of the electroencephalography (EEG). The term BCI was coined by University of California, Los Angeles (UCLA) professor Jacques Vidal when he peer-reviewed various publications on the same topic. In the early 1970s, the research on BCI started at the UCLA, under the National Science Foundation grant as well as a contract from the Defense Advanced Research Project Agency (DARPA).
In 1988, research indicated the ability of noninvasive EEG to control physical objects. The study described the capabilities of EEG to start to stop and restart the movement of a robot. However, this was on a line drawn on the floor. Later research on in 1990 indicates that BCI had the bidirectional characteristic of controlling computer buzzer through anticipation of the brain potential, also known as the Contingent Negative Variation (CNV). The study described an experiment depicting the state of the brain, through the manifestation of the CNV, controlling the feedback loop. These resulted in the Elcctroexpectogram (EXG), which is a representation of the expected learning nature of the brain. In September 1999, development in the BCI research made progress by developing a single position switch that is brain controlled that responded to certain patterns that are detectable in the spatiotemporal EEG that was measured in the human scalp. The BCI society was officially launched in 2015.
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Classification of BCI Types
The BCI is grouped into three categories, and this is done based on the way the electrical signals that are obtained from the cells in the human brain.
- Invasive BCI Acquisition: this makes use of special devices to capture the signals from the brain. These devices are designed to make detection from a single area of the brain. They are often referred to as a single unit.
- Partially Invasive BCI Acquisition: The difference with the invasive device is the weaker quality signal captured from the human brain. The device, however, has the advantage of not leaving scars on the patient’s brains.
- Non-Invasive BCI Acquisition: this makes up them safest as well as the least costly devices. However, the devices are weaker in comparison to the human brain. They are unable to penetrate the human skull. This is among the most noninvasive method as per the recording of the EEG.
Current Development in the BCI Research
The BCI is the fastest among the fast-growing emerging technology. Through this field, scientists aim at building a direct channel through which the computer and the human brain can communicate. The objective of the study is to create a collaboration in which the brain sends out signals as well as controlling a mechanical device which represents the body.
Based on the functional magnetic resonance imaging (fMRI-BCI), the brain-computer interface makes it possible for the volitional control of certain regions of the human brain. The aim of the fMRI-BCI is to eliminate the drawbacks that are often associated with EEG-BCI, the provision of only low resolution as well as ambiguity in the localization of the neuron activities. The fMRI-BC I makes it possible to record the activities of the entire brain. The architectural model of the fMRI-BCI is made up of four components (1) the signal acquisition, (2) signal feedback, (3)the participant as well as the (4) signal analyzer all connected via a LAN. There are several areas of application for the fMRI-BCI, this includes emotion processing, rehabilitation of stroke patients and visual perception.
Several challenges are facing the current research on the BCI, and this includes;
High error rate due to the low transfer rate of data as well as the low signal strength. This results from a high rate of error. Additionally, the brain signal has high variation, and this also results in high rates of error.
Data transfer rate – despite the several experiments that have been performed, the best data transfer rate for the subject has 3 bits of data. The low data transfers make the BCI application suffer regarding response, therefore, lowering the application control accuracy.
Lack of accuracy in classification of the signals: the classification of the captured signals from the brain is a challenge to the majority of researchers, this is due to the high interference that is experienced due to brain activities. Some of the proposed classification techniques of the brain signals include the computational intelligence technique.
Future Application of BCI
This technology is used in the Bone-Conduction, this is where patients are offered alternative ways of delivery stimulation inputs to weaker organs such as the hands. According to Musk, the BCI can be used in a bidirectional way, and this could aid in improving the human brain memory, therefore, making man smarter. This would aid in decision making as well as provide an extension to the mind of humans.
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Davide Valeriani, a UK based researcher, uses the EEG-BCI to read the minds of unconscious individuals while making decisions. Through this experiment, Vleriani hopes have a deeper understanding of the human brains and how decisions are made. He notes that BCI has for long be sidelined for people with disabilities. However, this can be used as a core tool that can enable humans go beyond their limits, hence improving the life of humanity.
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