Background of BCI
Brain computer interfacing is the maturing technology for the interfacing human brain to the computer. It is a method of commutation based on the neural activity generated by the brain and independent of its normal pathway of peripherals and muscles. The neural activity used in the Brain Computer Interface(BCI) can be recorded using invasive and noninvasive techniques. The goal of BCI is not to determine the person’s intention by strictly listening brain activity but allow the user to interact with the device. The potential of brain computer interfacing systems for helping handicapped people is obvious. There are many computer interfaces designed for the disabled persons. Most of
these are require some sort of reliable muscular control such as neck, head, eyes, or other facial muscles. [1]
In 1929 Hans Berger created a device for recording the electrical activity of brain is known as the Electroencephalography. By using these signal device could be controlled. As reviewed by wolpow and collegeous, 40 years later in the 1970’s researchers were able to develop primitive control systems based on electrical activity recorded from the head. Work by Vidal and other groups provided that the signals from the brain activity could be used to effectively communicate a user’s intent. The current BCI based tools can be aid users in communication, daily living activities, environmental
control, momentum, and exercise, with limited success and mostly in research. [1]
This technology used for the EEG electrode directly gives the information about human brain. As in the case of the physiological sensors information from these neurophysiological sensors can be used to provide more contexts that helps us to interpret a user’s activities and desires. In addition, brain activity can be controlled by the user and it can be used to control an application. Hence a user can decide to use his or her brain activity to issue commands. One example is motor imagery, where the user imagines a certain movement in order to for example, navigate in a virtual or physical environment.
On the other hand, an environment can attempt to issue signals from which it can become clear, by looking at the initiated brain activity, what the user is interested in or wants to achieve. The advances in cognitive neuroscience and brain imaging technologies provide us with the increasing ability to interface directly with activity in the brain. Researchers have begun use these technologies to build brain-computer interfaces. Originally, these interfaces were meant to allow patients with severe motor
disabilities to communicate and to control devices by thought alone. Removing the need for motor movements in computer interfaces is challenging and rewarding, but there is also the potential of brain sensing technologies as input mechanisms that give access to extremely rich information about the state of the user. Having access to this information is valuable to Human-Computer Interaction researchers and opens up at least three distinct areas of research: controlling computers by using thought alone or as a complementary input modality, evaluating systems and interfaces, and building adaptive user interfaces. [2]
From 20 years researches have created models of several working BCI systems. One such BCI system was developed by Farwell and Donchin. They created a BCI system that could be used to type out words by selecting letters, words, and commands from the matrix on the screen while the user thought about the next letters he or she wanted. When the expected letter generated on the screen, the user would generate detectable P300. Because of the system focused on detecting only P300s, signal
acquisition done at specific electrodes. Feature extraction generates 36 vectors, one for each square on the screen. As the P300 is time locked to the stimulus, when a particular row or column was flashed, a 600ms window of signal was added in to each of corresponding feature vectors. The translation was done by continuously ranking all the features using various methods. The letter, word, or command corresponding to the highest ranked feature vector was classified as the user’s intent. [3]
Kerin and aunon developed a BCI system that allowed user with severe physical disabilities to communicate with their surroundings by spelling specific code words that were predefined commands. Depending on the cognitive task performed by the user, the system could detect differences in lateralized spectral power levels. Because the cognitive tasks were not defined, EEG signals were collected from electrodes covering the parental and occipital regions. Feature extraction involved generating two features vectors using fast Fourier transform and auto regressive spectral estimation methods and then running them through a band pass filter in four frequency bands. The Bayesian quadratic classifier performed feature translation based on the power or AR coefficients
of the features. [3]
References:
1) Bin He, “Neural engineering”, 2005, volume 3, page numbers [85, 86]
2) Desney S. Tan .Anton Nijholt, “Brain Computer Interfaces Applying Our Minds To
Human Computer Interaction”,2010, page number [vi, 7, 8, 9 ]
3) Bin He, “Neural engineering”, 2005, volume 3, page numbers [114,115]
these are require some sort of reliable muscular control such as neck, head, eyes, or other facial muscles. [1]
In 1929 Hans Berger created a device for recording the electrical activity of brain is known as the Electroencephalography. By using these signal device could be controlled. As reviewed by wolpow and collegeous, 40 years later in the 1970’s researchers were able to develop primitive control systems based on electrical activity recorded from the head. Work by Vidal and other groups provided that the signals from the brain activity could be used to effectively communicate a user’s intent. The current BCI based tools can be aid users in communication, daily living activities, environmental
control, momentum, and exercise, with limited success and mostly in research. [1]
This technology used for the EEG electrode directly gives the information about human brain. As in the case of the physiological sensors information from these neurophysiological sensors can be used to provide more contexts that helps us to interpret a user’s activities and desires. In addition, brain activity can be controlled by the user and it can be used to control an application. Hence a user can decide to use his or her brain activity to issue commands. One example is motor imagery, where the user imagines a certain movement in order to for example, navigate in a virtual or physical environment.
On the other hand, an environment can attempt to issue signals from which it can become clear, by looking at the initiated brain activity, what the user is interested in or wants to achieve. The advances in cognitive neuroscience and brain imaging technologies provide us with the increasing ability to interface directly with activity in the brain. Researchers have begun use these technologies to build brain-computer interfaces. Originally, these interfaces were meant to allow patients with severe motor
disabilities to communicate and to control devices by thought alone. Removing the need for motor movements in computer interfaces is challenging and rewarding, but there is also the potential of brain sensing technologies as input mechanisms that give access to extremely rich information about the state of the user. Having access to this information is valuable to Human-Computer Interaction researchers and opens up at least three distinct areas of research: controlling computers by using thought alone or as a complementary input modality, evaluating systems and interfaces, and building adaptive user interfaces. [2]
From 20 years researches have created models of several working BCI systems. One such BCI system was developed by Farwell and Donchin. They created a BCI system that could be used to type out words by selecting letters, words, and commands from the matrix on the screen while the user thought about the next letters he or she wanted. When the expected letter generated on the screen, the user would generate detectable P300. Because of the system focused on detecting only P300s, signal
acquisition done at specific electrodes. Feature extraction generates 36 vectors, one for each square on the screen. As the P300 is time locked to the stimulus, when a particular row or column was flashed, a 600ms window of signal was added in to each of corresponding feature vectors. The translation was done by continuously ranking all the features using various methods. The letter, word, or command corresponding to the highest ranked feature vector was classified as the user’s intent. [3]
Kerin and aunon developed a BCI system that allowed user with severe physical disabilities to communicate with their surroundings by spelling specific code words that were predefined commands. Depending on the cognitive task performed by the user, the system could detect differences in lateralized spectral power levels. Because the cognitive tasks were not defined, EEG signals were collected from electrodes covering the parental and occipital regions. Feature extraction involved generating two features vectors using fast Fourier transform and auto regressive spectral estimation methods and then running them through a band pass filter in four frequency bands. The Bayesian quadratic classifier performed feature translation based on the power or AR coefficients
of the features. [3]
References:
1) Bin He, “Neural engineering”, 2005, volume 3, page numbers [85, 86]
2) Desney S. Tan .Anton Nijholt, “Brain Computer Interfaces Applying Our Minds To
Human Computer Interaction”,2010, page number [vi, 7, 8, 9 ]
3) Bin He, “Neural engineering”, 2005, volume 3, page numbers [114,115]
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