KR20170061317A - Realtime simulator for brainwaves training and interface device using realtime simulator - Google Patents
Realtime simulator for brainwaves training and interface device using realtime simulator Download PDFInfo
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Abstract
A real-time simulator for brain wave training and an interface device using the same are disclosed. At least one program loaded memory; And at least one processor, wherein the at least one processor acquires brain wave data measured by a user through an EEG measuring device after presenting an imagining operation for EEG training according to the control of the program; And extracting feature information corresponding to the imaging operation from the brain wave data to learn the characteristics of the user's brain wave with respect to the imaging operation.
Description
The following description relates to a simulator for EEG training and an interface device using the same.
This study was conducted as a result of the study of the following projects.
- IITP-2015-H8501-15-1019 (IITP-2015-H8501-15-1019) The University ICT Research Center of Future Creation Science and Information and Communication Technology Promotion Center
- The basic research project (2010-0020163), funded by the government (Ministry of Education) and funded by the Korea Research Foundation,
In the era of information explosion, "how to deliver" information rather than "how to generate" is important, which results in interface problems. As interfaces and system paradigm shifts to human-centricity become more and more necessary to develop underlying technologies, interfaces will be an effective means of reducing gaps between products and users.
Recently, research on interfaces has been studied with various approaches other than mouse and keyboard in the past. In the course of various studies, the bio-signal-based interface technology is a human-friendly interface technology that utilizes artificial bio-signals such as electrocardiograms and brain waves, and is being studied as a next generation user interface after text / voice / gestures. Although bio-signal-based interface technology has been studied extensively in Korea and abroad, it has yet to show practical technology that is commercially available.
Recently, BCI (Brain Computer Interface), which is actively studied, is an interface technology using brain waves. It is introduced interface technology to control cursor through brain wave and BCI model to control automobile operation in 3D virtual environment.
Korean Patent Application No. 1993-0021335 (entitled " Brake automatic control device using brain wave and method thereof "), Korean Patent Application No. 1996-057464 (invented) as an example of a technique for controlling an automobile brake using brain waves Name of the brake control device by detecting the brain waves of the driver).
However, the biggest problem of interface technology using EEG is that each person has different EEG characteristics and the ability to generate EEG also has individual differences. Therefore, in order to apply the human brain signal to the BCI, it is necessary to study the EEG signal training that the user can smoothly generate the EEG signal.
The present invention provides a rehabilitation system and method for training a user to generate an EEG signal smoothly through an EEG signal training.
The present invention provides a rehabilitation system and method for training a user's ability to generate brain waves for efficient use of an EEG-based interface device.
At least one program loaded memory; And at least one processor, wherein the at least one processor acquires brain wave data measured by a user through an EEG measuring device after presenting an imagining operation for EEG training according to the control of the program; And extracting feature information corresponding to the imaging operation from the brain wave data to learn the characteristics of the user's brain wave with respect to the imaging operation.
According to an aspect of the present invention, the at least one processor is configured to process a process of providing a guide or a comment related to the imaginary operation when the feature information is out of a critical range, under the control of the program, The acquisition process and the learning process may be repeatedly performed.
According to another aspect of the present invention, the learning process includes separating frequency bands necessary for EEG analysis from the EEG data using Fast Fourier Transform (FFT), and then extracting EEG data from the EEG data of the separated frequency band It is possible to extract the corresponding feature information and store the information according to the imaging operation through the machine learning.
According to another aspect of the present invention, the learning process includes analyzing a coefficient of regression analysis for the EEG data using a k-nearest neighbors algorithm, The learning model of the k-nearest neighbor algorithm can be generated by extracting feature information corresponding to the imaginary operation in the frequency band of the channel.
According to another aspect of the present invention, the learning process includes: a first step of analyzing a regression analysis decision coefficient for the acquired EEG data; A second step of selecting a channel and a frequency band for EEG learning according to the regression analysis decision coefficient analyzed in the first step; A third step of extracting feature information corresponding to the imaging operation in a frequency band of the selected channel in the second step; A fourth step of generating a learning model in which the feature information extracted in the third step is a training set; A fifth step of analyzing a regression analysis decision coefficient for EEG data acquired after the learning model is generated; A sixth step of selecting a channel and a frequency band for EEG learning according to the regression analysis decision coefficient analyzed in the fifth step; A seventh step of extracting feature information corresponding to the imaging operation in a frequency band of the channel selected in the sixth step; And an eighth step of comparing the characteristic information extracted in the seventh step with the training set and outputting a comparison result.
There is provided an interface device that uses the system of the above-described contents to set the user's brain wave characteristic as an interface variable for inputting a computer.
According to the embodiment of the present invention, the user's brain wave power can be improved by providing a training simulation for user's brain wave generation.
According to the embodiment of the present invention, BCI technology based on EEG can be realized by applying trained EEG as an interface variable for computer input.
1 is a view for explaining an example of the configuration of a rehabilitation system according to an embodiment of the present invention.
FIG. 2 is a diagram for explaining an example of an EEG training process of an EEG training apparatus according to an embodiment of the present invention.
3 is a diagram showing an example of the internal configuration of an EEG training apparatus according to an embodiment of the present invention.
4 and 5 are diagrams illustrating an example of an interface screen in which an EEG signal can be confirmed in an embodiment of the present invention.
6 is a diagram illustrating an example of an EEG signal learning process of the EEG training apparatus according to an embodiment of the present invention
7 is a block diagram for explaining an example of the internal configuration of a computer system according to an embodiment of the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
The present invention relates to a technique for providing a training simulation of a user's brain wave, and in order to use software based on an EEG signal, a simulation service including generation practice, measurement, judgment, etc. for a user's EEG signal .
The present invention aims to develop a simulator for training the user's ability to generate brain waves for efficient use of an EEG signal based interface device. In order to apply the human brain signal to the BCI, it is necessary to develop an EEG signal trainer to train the user to generate the EEG signal smoothly.
First, a background study related to the present invention will be described.
BCI (Brain Computer Interface)
BCI is defined as an interface technology that connects a human brain with a computer and controls the computer through brain waves, and widely belongs to HCI (Human Computer Interface) technology. In addition, BCI technology is sometimes referred to as BMI (Brain Machine Interface) because brain waves can manipulate machines such as wheelchairs and robots. The implementation of BCI technology involves measuring brain waves through a device that recognizes EEG stimuli, analyzing EEG through signal processing, and then issuing commands to input / output devices.
EEG brainwave )
Electroencephalogram (EEG) refers to a minute electrical activity of a brain cell population induced by attaching an electrode to the scalp, amplified by an electroencephalogram system, and recorded with the potential as the vertical axis and the time as the horizontal axis. In other words, EEG is a measure of electrical activity that occurs in the cerebral cortex. It changes in time and space according to brain activity, state at the time of measurement, and brain function. It has a frequency of 0 ~ 50Hz and an amplitude of 10 ~ 200uV. The EEG is classified into a delta wave, a delta wave, a theta wave, an alpha wave, an alpha wave, a beta wave, and a beta wave depending on the frequency range . Table 1 shows the characteristics of the EEG according to frequency.
(delta wave)
(? wave)
Temporal
(alpha wave)
Occipital lobe
(beta wave)
(? Wave)
Occipital lobe
Measurement position of EEG
Electroencephalograms measure the electrical activity of the brain by attaching electrodes to the cerebral cortex. At this time, attach the electrode according to International10-20System which specifies the attachment position of the electrode on the scalp. In International10-20 System, C means Central, F means Frontallobe, P means Parietallobe, O means Occipitallobe, and T means Temporallobe. The numbers after the alphabet mean the right hemisphere, which is the odd number, and the right hemisphere, which is the even number.
The position of the electrode is indicated by the center line connecting the division point (Naison) between the eye and the forehead and the division point (Inion) between the neck and the back of the head, and the middle part is called Cz. Based on the Cz point, the location of the EEG was determined by dividing 20% of the length in the direction of Naison and Inion, 20% in front of it, and 10% in front of it again. The name attached to it is International10-20 System.
EEG analysis and learning
In the present invention, it is possible to extract and train frequencies necessary for EEG analysis through Fourier transform, fast Fourier transform, and k-nearest neighbor algorithm of the above-described description for brain waves acquired from a user.
(1) Fourier Transform
Fourier transform refers to the decomposition of a waveform such as a voice into a fundamental frequency (base tone) and an angular frequency of each of the fundamental frequencies (each harmonic). In short, It is a method to calculate. All the waveforms can be represented by the sum of the fundamental wave and the high frequency wave through this Fourier transform. Therefore, if one waveform is continued, it is possible to know through which Fourier transform the waveform is composed.
(2) Fast Fourier Transform (Fast Fourier Transform)
Fast Fourier transform is abbreviated as FFT as one of algorithms for performing discrete Fourier transform (DFT).
When a computer performs discrete Fourier transform (DFT) of N data strings, it is necessary to multiply N2 times by calculating the Fourier coefficients of each frequency independently. However, if N is decomposed into a prime factor, the data string is divided and subjected to discrete Fourier transform (DFT) on a prime number group corresponding to each prime number, and finally N discrete Fourier transforms (DFT ), The number of operations is reduced. This effect is most significant when N is a power of 2, which requires only Nlog2N [times] multiplication. Therefore, the calculation time is significantly shortened when two-dimensional discrete Fourier transform (DFT) of an image, which is a two-dimensional data stream, is performed.
(3) k-nearest neighbors algorithm
The training data is a vector in a multidimensional feature space each having an item category name. The training phase of the algorithm is only to store the feature vector and item category name of the training specimen.
In the classification step, k is a user-defined constant and the vector without the classification (query or verification point) is classified by assigning the most frequent category names among the k training samples.
The most commonly used distance measure in continuous variables is the Euclidean distance. For discrete variables such as character classification, other measures such as the overlap distance (or hamming distance) may be used. For example, in the case of gene expression microarray data, k-NN uses correlation coefficients such as Pearson and Spearman only. Often, a distance measure with a large margin can improve the accuracy of k-NN classification significantly if it is learned by a special algorithm such as nearest neighbors or neighborhood component analysis.
The disadvantage of the classification according to the "majority vote" appears when the item distribution is biased. In other words, data of more frequent items tend to dominate the prediction of new data. This is because the data of the more frequent items tend to be the majority and become the majority of the k closest neighbors. One way to solve this problem is to weight the classification by considering the distance between each verification point and k nearest neighbors. Multiply the k items of the nearest neighbors (in the case of regression problems) by a weight inversely proportional to the reciprocal of the distance from that point to the verification point. Another way to solve the bias is to abstract the data representation. For example, in a self-organizing map (SOM), each node is a cluster representative (center) of similarities regardless of the original training data density. k-NN can also be applied to SOM.
In addition, in the present invention, an independent component analysis (ICA) may be used to remove unnecessary noise of the measured EEG, and a power spectrum analysis may be applied to acquire frequency information of brain waves And a band pass filter can be used to extract a frequency band necessary for EEG analysis.
(6) Independent Component Analysis
Independent component analysis is a method of extracting original signals by separating signals from the result of mixing multiple signals and minimizing interference. Independent component analysis is one of the methods of Blind Source Separation (BSS) that finds the source of the signal through analysis of the signal in the absence of information about the generation position or generation path of linear signals.
Independent component analysis minimizes correlations and dependencies in signals of various dimensions, and can be classified into the most independent signals probabilistically by maximizing entropy. Electroencephalogram (EEG) is also a multidimensional signal that is a combination of linear signals measured from several electrodes. When the independent component analysis is applied to this signal, the brain waves obtained from the electrodes in the EEG signal can be separated into independent signals of the electrodes, so that data closest to the original EEG signals with minimal interference can be extracted. Thus, independent component analysis can be applied to isolate and remove unwanted noise measured with EEG such as eye flicker, eye movement, and muscle movement.
(7) Band Pass Filter
The EEG signal acquired from the user is composed of various frequency components even if the unnecessary noise is removed. In this integrated signal, only the frequency domain necessary for analysis should be extracted. For this purpose, frequency filtering is used. The types of the filters include a low pass filter which passes only a frequency signal lower than a specific frequency band according to the frequency domain, a high pass filter which passes only a frequency signal higher than a specific frequency band And a band pass filter for filtering a specific frequency band by passing a specific frequency band. Therefore, a frequency corresponding to brain waves can be extracted using a band-pass filter of a specific band (for example, 0 to 50 Hz).
(6) Power Spectrum Analysis
Although it is convenient to observe the EEG pattern as an analog time series data that changes with time, it is not enough to analyze by analyzing the specific information.
Power spectral analysis converts the time series analogue signal, EEG, into frequency domain through FFT. The signal transformed into the frequency domain can confirm the distribution and the density of the frequency components. In this spectrum analysis, the power value is mainly used, which is expressed as the square of the amplitude (㎶ 2 ). It is possible to calculate the power value by spectral analysis with very short time EEG measurement. However, in order to better represent the electrical activity of the brain, the power value is calculated for the refined EEG, which is removed from the noise of about 20-30 seconds to 3 minutes per frequency band.
1 is a view for explaining an example of the configuration of a rehabilitation system according to an embodiment of the present invention. Referring to FIG. 1, the rehabilitation system according to the present invention includes an
The
The
The
As can be seen from the characteristics of the EEG signal according to frequency division, the EEG signal has unique characteristic information for each imagined motion. As a simple example, in the case of logical actions such as mathematical calculations, the left brain wave is activated compared to the right brain wave, and the right brain wave can be activated compared to the left brain wave in the case of emotional actions such as reminiscent of good memory or sick memory. When the user imagines a specific operation, an EEG signal including feature information corresponding to the operation is generated. Since the characteristics of each user's brain waves are different, it is very difficult to distinguish the imaginary motion from the brain waves, Therefore, it is difficult to acquire the EEG signal containing the accurate feature information when there is a concern or difficulty concentrating in the process of operation imagination. However, repeating the regular practice of the operation imagination can improve the user's ability to generate EEG by learning the user's EEG characteristics and increasing the concentration for EEG signal generation. To this end, the
FIG. 2 is a diagram for explaining an example of an EEG training process of an EEG training apparatus according to an embodiment of the present invention.
1. An
2. The
3. The
4. The
5. The
6. The
Accordingly, in the present invention, the user can imagine the action suggested by the
3 is a diagram showing an example of the internal configuration of an EEG training apparatus according to an embodiment of the present invention.
3, the
The EEG signal generated by the user's imagination may be measured by the
The
The
In this case, DAMV means the absolute difference average value of the signal, which is a value obtained by integrating the absolute value of the signal for a predetermined time. Where x is the measured signal, i is the order of the samples, and N is the number of samples. This condition applies equally to the calculation of other feature points. DAMV is also referred to as average amplitude change (AAC) in other words.
Here, MAV denotes an absolute value of the absolute value of each signal amplitude as an absolute integral value.
Here, VAR is used to estimate the power density of the signal. In general, VAR is called signal variance and is defined as the mean square value of the signal deviation.
Here, RMS means the root-mean-square (RMS) of the signal, which is a variable with high magnitude and relevance of the power over time. It has an advantage that it can be easily calculated by a mathematical method without modifying the original signal of the signal.
The
The specific operation of the
The user-defined
The determining
At this time, the
The
The EEG training method according to the present invention may be composed of at least two operations based on the details described with reference to FIG. For example, the EEG training method includes an EEG receiving step of receiving an EEG signal generated by a user's motion imagery; A signal separation step of separating the received EEG signal according to a frequency range; A learning step of extracting minutiae points of the separated EEG signal and applying machine learning and storing information according to the motion imagery; Extracting characteristic points of the EEG signal measured after the learning step and applying machine learning to store information according to the operation imagery; And a user-defined step of determining a channel and a frequency band with high accuracy through analysis of decision coefficients in a machine learning algorithm; And a determination step of comparing information stored in the learning step with information stored in the guessing step.
6 is a diagram illustrating an example of an EEG signal learning process of the EEG training apparatus according to an embodiment of the present invention. Each step of the EEG signal learning process can be performed by the
The
The
The
The
The process of analyzing the regression analysis decision coefficient (r 2 ) (607, 612) is as follows.
(1) where x is a set 1 and y is a set 2, a regression analysis decision coefficient (r 2 ) can be defined as shown in Equation (5).
here,
In other words,Covariance
Lt; / RTI >Each variance
.The learning model for the EEG signal is generated based on the result calculated for each channel and frequency through the above equation. The higher the regression analysis decision coefficient (r 2 ), the better the channel and frequency to distinguish the two data sets. For example, the
Accordingly, the present invention provides a simulator for learning the characteristics of the user's brain waves and training the user's ability to generate brain waves for efficient use of the EEG signal-based interface device.
7 is a block diagram for explaining an example of the internal configuration of a computer system in an embodiment of the present invention. The
The
The input /
The
The
7 is merely an example of a
The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented as a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a programmable logic unit, a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.
The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.
The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.
While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.
Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.
Claims (6)
At least one processor
Lt; / RTI >
Wherein the at least one processor, under control of the program,
A process of acquiring measured EEG data from a user through an EEG measurement device after presenting imaginary motion for EEG training; And
Extracting feature information corresponding to the imaging operation from the brain wave data to learn the characteristics of the user's brain wave with respect to the imaging operation
Lt; / RTI >
Wherein the at least one processor, under control of the program,
A step of providing a guide or a comment related to the imaginary operation when the feature information is out of the threshold range
Lt; / RTI >
After the process of providing is performed, the process of acquiring and the process of repeating the learning process
Lt; / RTI >
The learning process includes:
Extracting a frequency band necessary for EEG analysis from the EEG data using Fast Fourier Transform, extracting feature information corresponding to the imaging operation from brain wave data of the separated frequency band, Storing information according to the imaginary operation
Lt; / RTI >
The learning process includes:
analyzing a regression analysis decision coefficient for the EEG data using a k-nearest neighbors algorithm, and calculating a feature corresponding to the imaginary motion in a frequency band of the selected channel according to the regression analysis decision coefficient And generating a learning model of the k-nearest neighbor algorithm
Lt; / RTI >
The learning process includes:
A first step of analyzing a regression analysis decision coefficient for the obtained EEG data;
A second step of selecting a channel and a frequency band for EEG learning according to the regression analysis decision coefficient analyzed in the first step;
A third step of extracting feature information corresponding to the imaging operation in a frequency band of the selected channel in the second step;
A fourth step of generating a learning model in which the feature information extracted in the third step is a training set;
A fifth step of analyzing a regression analysis decision coefficient for EEG data acquired after the learning model is generated;
A sixth step of selecting a channel and a frequency band for EEG learning according to the regression analysis decision coefficient analyzed in the fifth step;
A seventh step of extracting feature information corresponding to the imaging operation in a frequency band of the channel selected in the sixth step; And
An eighth step of comparing the feature information extracted in the seventh step with the training set and outputting a comparison result;
≪ / RTI >
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