The brain-computer interface is a direct communication and control channel established between the human brain and a computer or other electronic device. Through this channel, people can express ideas or manipulate devices directly through the brain without the need for language or action. It can effectively enhance the ability of patients with severe physical disabilities to communicate with the outside world or control the external environment to improve the quality of life of patients. Brain-computer interface technology is a cross-disciplinary technology involving neuroscience, signal detection, signal processing, and pattern recognition.
background
Direct communication with the outside world through the signals of brain thinking activities, and even the control of the surrounding environment, is the dream that humans have pursued since ancient times. Since HansBerger first recorded EEG in 1929, it has been speculated that it may be used for communication and control, so that the brain does not need the usual medium - peripheral nerves and limbs to help directly to the outside world. However, due to the limitations of the overall level of science and technology at the time, and the lack of understanding of the brain's thinking mechanism, little progress has been made in this area.
The Brain-Computer Interface (BCI) technology was formed in the 1970s (Vidal, 1973) and is a cross-disciplinary technology involving neuroscience, signal detection, signal processing, and pattern recognition. Over the past 20 years, with the improvement of people's understanding of the function of the nervous system and the development of computer technology, the research of BCI technology has shown an obvious upward trend. In particular, the two BCI international conferences in 1999 and 2002 have pointed out the development of BCI technology. The direction. At present, BCI technology has attracted the attention of many scientific and technological workers in the world, and has become a new research hotspot in the fields of biomedical engineering, computer technology and communication.
Introduction to BCI
BCI is a real-time communication system that connects the brain to external devices. The BCI system can directly convert the information sent by the brain into commands that can drive external devices, and replace human physical or linguistic organs to communicate with the outside world and control the external environment. In other words, the BCI system can replace normal peripheral nerves and muscle tissue, enabling communication between people and computers or between people and the external environment.
The core of BCI technology is to convert the EEG signal input by the user into a conversion algorithm that outputs control signals or commands. A very important part of the BCI research work is to adjust the mutual adaptation relationship between the human brain and the BCI system, that is, to find suitable signal processing and conversion algorithms, so that the neural electrical signals can be converted into real-time, fast and accurate through the BCI system. A command or operational signal that is recognized by the computer.
BCI system principle and concept
Neuroscience research shows that after the brain produces action consciousness and before the action is performed, or after the subject is stimulated by the outside world, the electrical activity of the nervous system changes accordingly. This change in neural electrical activity can be determined by certain means. Detected and used as the characteristic signal of the action. By classifying and identifying these characteristic signals, distinguishing the action intentions that cause the changes of EEG, and then programming in computer language, turning human thinking activities into command signals to drive external devices. To achieve the control of the external environment by the human brain without the direct involvement of muscles and peripheral nerves. This is the basic working principle of BCI.
The BCI definition given at the first BCI International Conference is: "Brain Computer Interface is a communication system that does not rely on normal output channels composed of peripheral nerves and muscles." BCI does not rely on the involvement of muscles and peripheral nerves to directly communicate between the brain and the computer. This is an adjuvant treatment and language for patients who are completely incapable (such as stroke, amyotrophic (spinal) lateral sclerosis, cerebral palsy, etc.). The restoration of functions and behavioral abilities, the control of external devices in special environments, and even the improvement of entertainment methods are of great significance.
Basic structure of BCI system
Based on a variety of different needs, people have designed a variety of EEG-based BCI prototype systems that can be demonstrated in the lab. In principle, BCI systems generally consist of functional inputs such as input, output, and signal processing and conversion. The function of the input link is to generate and detect EEG activity characteristic signals containing certain characteristics, and to describe the characteristics with such parameters. The function of signal processing is to process and analyze the source signal, and convert the continuous analog signal into a digital signal represented by certain characteristic parameters (such as amplitude, autoregressive model coefficients, etc.) to facilitate reading and processing by the computer. And identifying and classifying these characteristic signals to determine their corresponding thought activities. Signal conversion is to generate driving or operating commands according to the characteristic signals obtained after signal analysis and classification, to operate the output device, or directly output letters indicating the patient's intention or Words, to achieve the purpose of communicating with the outside world. As an intermediate link between input and output, signal analysis and conversion is an important part of the BCI system. Improved signal analysis and conversion algorithms can improve the accuracy of classification to optimize the control performance of BCI systems. Output devices of the BCI system include pointer motion, character selection, motion of the neural prosthesis, and control of other devices.
BCI classification
The first BCI International Conference divided the BCI systems into two broad categories based on the nature of the input signal: a BCI system using spontaneous EEG signals and a BCI system using EEG signals.
The BCI system based on spontaneous EEG is the application of spontaneous EEG as the input characteristic signal of the system. It is characterized in that after training, the subject can control the brain electrical changes autonomously, thereby directly controlling the external environment, but usually requires a large amount of training for the subject, which is susceptible to various factors such as physical condition, mood, and condition. influences.
The BCI system that induces EEG signals uses external stimuli to induce changes in the electrical activity of the corresponding parts of the cerebral cortex, and uses it as a characteristic signal. The externally induced BCI system does not require excessive training for the subject, but requires a specific environment (such as a matrix of flicker visual stimulus inputs), which is not conducive to the promotion and application of the system.
In the system output mode, the former enables the operator to move the pointer to any two-dimensional or multi-dimensional position, while the latter only allows the operator to select among the listed options. Depending on the way the signal is detected, the BCI can also be divided into two basic forms: the electrode built-in type and the electrode external type.
The electrode built-in signal detection method makes the electrode directly contact with the cerebral cortex or enter the cerebral cortex, and the measured signal noise is small and the loss is low. However, due to the complicated operation involved, the operation is complicated, the professional technician is required, and the infection is easy.
The external signal detection method of the electrode is simple and safe, and is beneficial to the promotion of the BCI system. However, since the electrode is far away from the signal source, the noise is large. In the BCI system design, which scheme should be used according to the characteristics of the signal and the measurement technique The level and the accuracy of the actual requirements are considered together.
BCI key technology
The BCI system consists of signals generation, processing, conversion, output, and switches and clocks. In the development of BCI technology, signal analysis and conversion algorithms are the most important research content.
Source signal acquisition
The acquisition process of the BCI source signal includes signal generation, detection (electrode recording), signal amplification, denoising, and digitization processing.
The human brain is capable of producing a variety of signals, including electrical, magnetic, chemical, and mechanical responses to brain activity. These signals can be detected by the corresponding sensors, making the implementation of BCI possible. Since the detection technology of signals such as magnetic and chemical needs higher requirements, the current acquisition of BCI signals is mainly based on EEG detection technology with relatively simple technology and low cost.
Signal generation
Depending on the nature and nature of the signal to be acquired, a corresponding method of generating the characteristic signal must be taken. Signal generation methods include the use of visual evoked potentials, the use of event-related potentials, the simulation of virtual environments, and the autonomous control of EEG.
Signal detection
The detection method of the signal depends on the nature of the electrical signal to be measured. According to the electrode type, the BCI system can be divided into two basic forms: the electrode built-in type and the electrode external type.
Signal processing method
The signal processing in BCI system includes signal preprocessing, feature extraction, recognition and classification. The traditional EEG signal analysis method is to detect the signal multiple times and perform mean filtering, and then use statistical methods to find the change law of EEG. This method has low information transmission rate and can not meet the requirements of real-time control. At present, the processing of EEG signals generally uses single training signals. Among them, feature extraction and recognition classification are the most critical links in BCI signal processing.
Feature extraction method in BCI
Feature extraction is to use the characteristic signal as the source signal, determine various parameters and use this as a vector to represent the feature vector of the signal feature. The feature parameters include time domain signals (such as amplitude) and frequency domain signals (such as frequency). The corresponding feature extraction methods are also divided into time domain method, frequency domain method and time-frequency domain method.
Classification and recognition of characteristic signals
The classification of characteristic signals is based on the characteristics that EEG signals can make different responses of EEG activities according to different movements or consciousness, and determine the relationship between the type of motion or consciousness and the characteristic signals. The quality of signal classification depends on two aspects. The factors are: whether the characteristic signal to be classified has obvious characteristics, that is, the nature of the characteristic signal; and second, whether the classification method is effective. Several representative BCI feature signal classifications are summarized as follows: artificial neural network; Bayesian-Kalman filtering; linear discriminant analysis; genetic algorithm; probability model.
BCI application
As a multi-disciplinary emerging communication technology, at present, BCI research is mostly in the theoretical and laboratory stages, and there is still a certain gap from the actual application. However, from the perspective of its performance, the BCI system and its technology will play an important role in various fields involving the human brain, especially for the ability recovery and functional training of patients with severely disabled activities. At present, the research on BCI applications mainly focuses on the following aspects:
Communication function
The purpose of this type of research is to improve the ability of patients with loss of language function to communicate with the outside world.
Environmental control
At present, the research on BCI environmental control is mainly based on virtual reality technology. Virtual reality has the characteristics of relative security and target mobility, which can provide a safe and reliable environment for training and adjusting nervous system activities. The subject's brain issues an operational command that is not transmitted and executed by the muscles and peripheral nerves. Instead, the BCI system detects, analyzes, and identifies the corresponding EEG signals to determine the operation to be performed, and then outputs the pair. The target is controlled.
Motor function recovery
The BCI system completes the detection and classification recognition process of the EEG signal, and then outputs the command to the neural prosthesis, completes the function of the peripheral nerve that has lost the function, or outputs the command signal to the command receiving system of the wheelchair to complete the exercise. The function of walking, etc., enables patients who have completely lost their functions in the limbs to perform some simple activities or perform functional auxiliary training without taking care of themselves.
Other applications
In theory, as long as there is a communication system involving neuroelectricity, BCI technology can be applied. For example, a driverless car suitable for disabled people is a series of changes in the EEG signal during operation, which is converted by the BCI system in real time. In order to achieve the purpose of no direct driving.
background
Direct communication with the outside world through the signals of brain thinking activities, and even the control of the surrounding environment, is the dream that humans have pursued since ancient times. Since HansBerger first recorded EEG in 1929, it has been speculated that it may be used for communication and control, so that the brain does not need the usual medium - peripheral nerves and limbs to help directly to the outside world. However, due to the limitations of the overall level of science and technology at the time, and the lack of understanding of the brain's thinking mechanism, little progress has been made in this area.
The Brain-Computer Interface (BCI) technology was formed in the 1970s (Vidal, 1973) and is a cross-disciplinary technology involving neuroscience, signal detection, signal processing, and pattern recognition. Over the past 20 years, with the improvement of people's understanding of the function of the nervous system and the development of computer technology, the research of BCI technology has shown an obvious upward trend. In particular, the two BCI international conferences in 1999 and 2002 have pointed out the development of BCI technology. The direction. At present, BCI technology has attracted the attention of many scientific and technological workers in the world, and has become a new research hotspot in the fields of biomedical engineering, computer technology and communication.
Introduction to BCI
BCI is a real-time communication system that connects the brain to external devices. The BCI system can directly convert the information sent by the brain into commands that can drive external devices, and replace human physical or linguistic organs to communicate with the outside world and control the external environment. In other words, the BCI system can replace normal peripheral nerves and muscle tissue, enabling communication between people and computers or between people and the external environment.
The core of BCI technology is to convert the EEG signal input by the user into a conversion algorithm that outputs control signals or commands. A very important part of the BCI research work is to adjust the mutual adaptation relationship between the human brain and the BCI system, that is, to find suitable signal processing and conversion algorithms, so that the neural electrical signals can be converted into real-time, fast and accurate through the BCI system. A command or operational signal that is recognized by the computer.
BCI system principle and concept
Neuroscience research shows that after the brain produces action consciousness and before the action is performed, or after the subject is stimulated by the outside world, the electrical activity of the nervous system changes accordingly. This change in neural electrical activity can be determined by certain means. Detected and used as the characteristic signal of the action. By classifying and identifying these characteristic signals, distinguishing the action intentions that cause the changes of EEG, and then programming in computer language, turning human thinking activities into command signals to drive external devices. To achieve the control of the external environment by the human brain without the direct involvement of muscles and peripheral nerves. This is the basic working principle of BCI.
The BCI definition given at the first BCI International Conference is: "Brain Computer Interface is a communication system that does not rely on normal output channels composed of peripheral nerves and muscles." BCI does not rely on the involvement of muscles and peripheral nerves to directly communicate between the brain and the computer. This is an adjuvant treatment and language for patients who are completely incapable (such as stroke, amyotrophic (spinal) lateral sclerosis, cerebral palsy, etc.). The restoration of functions and behavioral abilities, the control of external devices in special environments, and even the improvement of entertainment methods are of great significance.
Basic structure of BCI system
Based on a variety of different needs, people have designed a variety of EEG-based BCI prototype systems that can be demonstrated in the lab. In principle, BCI systems generally consist of functional inputs such as input, output, and signal processing and conversion. The function of the input link is to generate and detect EEG activity characteristic signals containing certain characteristics, and to describe the characteristics with such parameters. The function of signal processing is to process and analyze the source signal, and convert the continuous analog signal into a digital signal represented by certain characteristic parameters (such as amplitude, autoregressive model coefficients, etc.) to facilitate reading and processing by the computer. And identifying and classifying these characteristic signals to determine their corresponding thought activities. Signal conversion is to generate driving or operating commands according to the characteristic signals obtained after signal analysis and classification, to operate the output device, or directly output letters indicating the patient's intention or Words, to achieve the purpose of communicating with the outside world. As an intermediate link between input and output, signal analysis and conversion is an important part of the BCI system. Improved signal analysis and conversion algorithms can improve the accuracy of classification to optimize the control performance of BCI systems. Output devices of the BCI system include pointer motion, character selection, motion of the neural prosthesis, and control of other devices.
BCI classification
The first BCI International Conference divided the BCI systems into two broad categories based on the nature of the input signal: a BCI system using spontaneous EEG signals and a BCI system using EEG signals.
The BCI system based on spontaneous EEG is the application of spontaneous EEG as the input characteristic signal of the system. It is characterized in that after training, the subject can control the brain electrical changes autonomously, thereby directly controlling the external environment, but usually requires a large amount of training for the subject, which is susceptible to various factors such as physical condition, mood, and condition. influences.
The BCI system that induces EEG signals uses external stimuli to induce changes in the electrical activity of the corresponding parts of the cerebral cortex, and uses it as a characteristic signal. The externally induced BCI system does not require excessive training for the subject, but requires a specific environment (such as a matrix of flicker visual stimulus inputs), which is not conducive to the promotion and application of the system.
In the system output mode, the former enables the operator to move the pointer to any two-dimensional or multi-dimensional position, while the latter only allows the operator to select among the listed options. Depending on the way the signal is detected, the BCI can also be divided into two basic forms: the electrode built-in type and the electrode external type.
The electrode built-in signal detection method makes the electrode directly contact with the cerebral cortex or enter the cerebral cortex, and the measured signal noise is small and the loss is low. However, due to the complicated operation involved, the operation is complicated, the professional technician is required, and the infection is easy.
The external signal detection method of the electrode is simple and safe, and is beneficial to the promotion of the BCI system. However, since the electrode is far away from the signal source, the noise is large. In the BCI system design, which scheme should be used according to the characteristics of the signal and the measurement technique The level and the accuracy of the actual requirements are considered together.
BCI key technology
The BCI system consists of signals generation, processing, conversion, output, and switches and clocks. In the development of BCI technology, signal analysis and conversion algorithms are the most important research content.
Source signal acquisition
The acquisition process of the BCI source signal includes signal generation, detection (electrode recording), signal amplification, denoising, and digitization processing.
The human brain is capable of producing a variety of signals, including electrical, magnetic, chemical, and mechanical responses to brain activity. These signals can be detected by the corresponding sensors, making the implementation of BCI possible. Since the detection technology of signals such as magnetic and chemical needs higher requirements, the current acquisition of BCI signals is mainly based on EEG detection technology with relatively simple technology and low cost.
Signal generation
Depending on the nature and nature of the signal to be acquired, a corresponding method of generating the characteristic signal must be taken. Signal generation methods include the use of visual evoked potentials, the use of event-related potentials, the simulation of virtual environments, and the autonomous control of EEG.
Signal detection
The detection method of the signal depends on the nature of the electrical signal to be measured. According to the electrode type, the BCI system can be divided into two basic forms: the electrode built-in type and the electrode external type.
Signal processing method
The signal processing in BCI system includes signal preprocessing, feature extraction, recognition and classification. The traditional EEG signal analysis method is to detect the signal multiple times and perform mean filtering, and then use statistical methods to find the change law of EEG. This method has low information transmission rate and can not meet the requirements of real-time control. At present, the processing of EEG signals generally uses single training signals. Among them, feature extraction and recognition classification are the most critical links in BCI signal processing.
Feature extraction method in BCI
Feature extraction is to use the characteristic signal as the source signal, determine various parameters and use this as a vector to represent the feature vector of the signal feature. The feature parameters include time domain signals (such as amplitude) and frequency domain signals (such as frequency). The corresponding feature extraction methods are also divided into time domain method, frequency domain method and time-frequency domain method.
Classification and recognition of characteristic signals
The classification of characteristic signals is based on the characteristics that EEG signals can make different responses of EEG activities according to different movements or consciousness, and determine the relationship between the type of motion or consciousness and the characteristic signals. The quality of signal classification depends on two aspects. The factors are: whether the characteristic signal to be classified has obvious characteristics, that is, the nature of the characteristic signal; and second, whether the classification method is effective. Several representative BCI feature signal classifications are summarized as follows: artificial neural network; Bayesian-Kalman filtering; linear discriminant analysis; genetic algorithm; probability model.
BCI application
As a multi-disciplinary emerging communication technology, at present, BCI research is mostly in the theoretical and laboratory stages, and there is still a certain gap from the actual application. However, from the perspective of its performance, the BCI system and its technology will play an important role in various fields involving the human brain, especially for the ability recovery and functional training of patients with severely disabled activities. At present, the research on BCI applications mainly focuses on the following aspects:
Communication function
The purpose of this type of research is to improve the ability of patients with loss of language function to communicate with the outside world.
Environmental control
At present, the research on BCI environmental control is mainly based on virtual reality technology. Virtual reality has the characteristics of relative security and target mobility, which can provide a safe and reliable environment for training and adjusting nervous system activities. The subject's brain issues an operational command that is not transmitted and executed by the muscles and peripheral nerves. Instead, the BCI system detects, analyzes, and identifies the corresponding EEG signals to determine the operation to be performed, and then outputs the pair. The target is controlled.
Motor function recovery
The BCI system completes the detection and classification recognition process of the EEG signal, and then outputs the command to the neural prosthesis, completes the function of the peripheral nerve that has lost the function, or outputs the command signal to the command receiving system of the wheelchair to complete the exercise. The function of walking, etc., enables patients who have completely lost their functions in the limbs to perform some simple activities or perform functional auxiliary training without taking care of themselves.
Other applications
In theory, as long as there is a communication system involving neuroelectricity, BCI technology can be applied. For example, a driverless car suitable for disabled people is a series of changes in the EEG signal during operation, which is converted by the BCI system in real time. In order to achieve the purpose of no direct driving.
Turbines consist of runners, turbine boxes, nozzle assemblies, inlet pipes, deflectors and more. The runner is assembled by torque transmission by the shaft and pawn of the generator.
The centerline of the jet is in the same plane as the runner because the uses the dynamic force of the high pressure water stream to rotate the runner and forces the rotor to work while it is rotating.
Turbines have the advantages of dense construction, stable running and easy operation. Small turbines that are usually organized with horizontal shafts and 1-2 nozzles Normal size and larger turbines are most often organized by vertical shafts and 4 or 6 to get more force Equipped on the nozzle.
Water Turbine,Hydraulic Water Turbine,Water Turbine Generator,Water Turbine Power
Shenyang Zhicheng Heavy Machinery Manufacturing Co., Ltd. , https://www.zhichengmachinery.com