KR100798475B1 - Apparatus and control method for brain biofeedback by using color - Google Patents

Abstract

The present invention provides a neurofeedback device using a color and a control method thereof, comprising: a headset unit for measuring brain waves of a user in response to a color generated from a color display unit; A neurofeedback processing unit connected to the headset unit in a wired or wireless manner and extracting a specific color in response to a specific brain wave of the user by controlling the color displayed on the color display unit to be changed according to the brain wave signal measured by the headset unit; Connected to the neurofeedback processing unit by wire or wirelessly, the color display unit for changing and displaying the color under the control of the neurofeedback processing unit; comprising, by unconsciously extracting the preferred color in the brain to solve the brain dysfunction It will be possible.

Neurofeedback, Biofeedback, Hyperactivity Disorder, EEG, Sensor, Color, Unconscious

Description

Neurofeedback device using color and its control method {Apparatus and control method for brain biofeedback by using color}

1 is a conceptual diagram of a neurofeedback apparatus using colors according to an embodiment of the present invention.

FIG. 2 is a detailed block diagram of the headset unit in FIG. 1.

3 is a perspective view illustrating an embodiment of a headset unit in FIG. 2.

4 is a perspective view illustrating a mounting state of the headset unit in FIG. 3.

5 is a front view of FIG. 3.

6 is a plan view of FIG. 3.

7 is a detailed block diagram of the neurofeedback processing unit in FIG. 1.

FIG. 8 is a front view illustrating the front of the neurofeedback processing unit of FIG. 7.

FIG. 9 is a rear view of the neurofeedback processor of FIG. 7.

FIG. 10 is a detailed block diagram of the color display unit of FIG. 1.

11 is a flowchart illustrating a control method of a neurofeedback device using colors according to an embodiment of the present invention.

12 and 13 are flowcharts illustrating a control method in a reference mode of a neurofeedback device using colors according to an embodiment of the present invention.

14 and 15 are flowcharts illustrating a control method in a neurofeedback mode of a neurofeedback device using colors according to an embodiment of the present invention.

16 and 17 show examples of output of the neurofeedback mode in FIGS. 14 and 15.

FIG. 18 is a view showing an international 10-20 system for electrode placement when performing EEG measurement using a headset in the present invention.

Explanation of symbols on the main parts of the drawings

10: Headset part 11: Ag / Agcl sensor

12: isolation amplifier 13: amplifier

20: neurofeedback processing unit 21: LPF

22: notch filter 23: amplification unit

24: ADC 25: DSP

26: memory 27: signal transmitter

28; Result display unit 30: color display unit

31: signal receiver 32: color display driver

33: color indicator

The present invention relates to a neurofeedback device using a color and a control method thereof, and more particularly to a neurofeedback device using a color that is suitable for solving the brain dysfunction by unconsciously extracting the preferred color from the brain will be.

In general, neurofeedback refers to an operation of transferring a self-control of a living body, that is, changing a nerve state or a physiological state of the living body to some form of stimulus information and delivering the same to the living body. For example, in the case of EEG feedback, the electrode is attached to the subject's head and the EEG is collected, and whenever a certain frequency component (alpha) appears, a buzzer sounds to inform the examinee that a certain frequency component has appeared. In this case, the subject can always be trained while grasping his brain wave state, and thus will be able to arbitrarily emit a frequency wave (alpha). This shows that brain waves can be controlled by themselves, and is being studied as a treatment for essential hypertension, tension headache, migraine, arrhythmia, epilepsy, asthma, anxiety neurosis and insomnia.

BCI (Brain Computer Interface) is used for this purpose. These BCIs are communication systems in which messages or commands sent to the outside world by individuals do not go through the peripheral nerves, which are the normal output pathways of the brain. BCI is implemented by controlling electrical signals that can be measured in the brain or by using communication systems. Electrical signals measured in the brain are obtained noninvasively from the scalp and are represented by electroencephalograms (EGEs). Future BCI systems will monitor EEG activity in real time, and people will use EEG to control any electronically communicated device or software via a local processor.

In the late 1980s and early 1990s, BCI began to emerge in Europe and the United States on the basis of ideas for solving the communication problems of many people with disabilities using powerful computers available to the field. An essential part of BCI development is well known for the neurofeedback (NF) regulation described earlier by Kamiya (1968). In Kamiya's research and more, people have shown that viewing EEG parameters on the screen allows them to manipulate their EEG parameters and enhance them with appropriate rewards such as scores, money, or simply courageous words. .

The main mechanism of NF is the arbitrary conditioning paradigm in which users learn to influence the electrical activity of their brains. EEG rhythm training, which may be appropriate for health and clinical popularity, was originally considered useful in traditional relaxation induction techniques associated with Zen meditation. It is a pathology characterized by epilepsy forming part of the treatment to prevent cramps and dysfunctional regulation of cortical activity such as attention deficit hyperactivity disorder (ADHD) in children. It is related. Usually a healthy individual can increase their theta / alpha ratio after only five sessions.

Thus, if a person can manipulate the parameters of his or her brain activity, these parameters can be used as signals to handle or stimulate various other types of external devices. The basic idea of brain-machine coordination is not only to control EEG parameters per session, for example to promote health, but also to use EEG parameters in order to use EEG parameters and external objects previously connected by means of a special interface called BCI. To use them.

BCI has several applications in locked-in patients, for example, who are unable to use their muscles within the medical community.

However, it is still very inconvenient to use the BCI system for people with disabilities and healthy individuals. For example, the Thought Translation Interface (TTI), one of the best BCI systems, has had a problem that even very well-adapted users would take months or longer to get very relevant results.

Accordingly, the conventional BCI system has a problem in that it is necessary to maintain a mental state that continuously concentrates on other kinds of mental states having a specific reflection in the internal image or the EEG pattern that the BCI can recognize well.

Accordingly, the present invention has been proposed to solve the conventional problems as described above, an object of the present invention is to unload the color unconsciously preferred in the brain neurofeedback device using color that can solve the brain dysfunction and its control To provide a method.

Neurofeedback device using the color according to an embodiment of the present invention to achieve the above object,

A headset unit for measuring brain waves of a user in response to the colors generated by the color display unit; A neurofeedback processing unit connected to the headset unit in a wired or wireless manner and extracting a specific color in response to a specific brain wave of the user by controlling the color displayed on the color display unit to be changed according to the brain wave signal measured by the headset unit; And a color display unit connected to the neuro feedback processing unit by wire or wirelessly and changing a color under the control of the neuro feedback processing unit.

In order to achieve the above object, a control method of a neurofeedback device using colors according to an embodiment of the present invention,

A neurofeedback processor comprising: a first step of receiving an EEG signal of the user measured by the headset unit and controlling a color display unit to output a reference color; A second step of determining, after the first step, the neurofeedback processing unit receives the brain waves measured by the headset unit to perform real-time EEG measurement, and performs color change by EEG analysis data to determine whether the colors constantly converge; And a third step of diagnosing the corresponding color as a color of a specific brain wave of the user when the colors output from the color display unit constantly converge in the second step.

Hereinafter, an embodiment according to the present invention as described above, a neurofeedback apparatus using colors and a control method thereof will be described with reference to the accompanying drawings.

1 is a conceptual diagram of a neurofeedback apparatus using colors according to an embodiment of the present invention.

As shown in the figure, the headset unit 10 for measuring the brain wave of the user in response to the color generated in the color display unit 30; Connected to the headset unit 10 in a wired or wireless manner, and controlling the color displayed on the color display unit 30 to change according to the EEG signal measured by the headset unit 10 so as to respond to a specific brain wave of the user. Neurofeedback processing unit 20 for extracting the color; And a color display unit 30 that is connected to the neurofeedback processing unit 20 by wire or wirelessly and changes and displays the color under the control of the neurofeedback processing unit 20.

FIG. 2 is a detailed block diagram of the headset unit in FIG. 1, FIG. 3 is a perspective view showing an embodiment of the headset unit in FIG. 2, FIG. 4 is a perspective view showing a mounting state of the headset unit in FIG. 3, and FIG. 5 is of FIG. 3. It is a front view, and FIG. 6 is a top view of FIG. In addition, FIG. 18 is a view showing an international 10-20 system for electrode placement when performing EEG measurement using a headset in the present invention.

The headset unit 10 includes a sensor 11 for measuring brain waves of a user in response to the color generated by the color display unit 30; An isolation amplifying unit 12 for isolating amplify the signal measured by the sensor 11; And an amplifying unit (AMP) for amplifying the isolation amplified signal from the isolation amplifying unit 12 and transmitting the amplified signal to the neurofeedback processing unit 20.

Neurofeedback device using a color according to an embodiment of the present invention, as shown in Figure 2, the sensor 11 for measuring the brain wave of the user in response to the color generated in the color display unit 30; An isolation amplifier 12 for isolating and amplifying the signal measured by the sensor 11; And an amplification unit 13 for amplifying the isolation amplified signal from the isolation amplification unit 12 and transmitting the amplified signal to the neurofeedback processing unit 20.

The sensor 11 is characterized in that composed of Ag / Agcl sensor.

As illustrated in FIG. 18, the sensor 11 sets an electrode to Fp1, Fp2, AF7, and AF8, and sets a reference state to Fpz.

7 is a detailed block diagram of the neurofeedback processing unit in FIG. 1, FIG. 8 is a front view showing the front of the neurofeedback processing unit in FIG. 7, and FIG. 9 is a rear view showing the back side of the neurofeedback processing unit in FIG. 7.

The neurofeedback processing unit 20 includes: a low pass filter (LPF) for low pass filtering the signal transmitted from the headset unit 10 via wire or wirelessly; A notch filter 22 for notching filtering the signal output from the LPF 21; An amplifier (AMP) for amplifying the output of the notch filter 22; An analog-to-digital converter (ADC) 24 that receives the analog output of the amplifier 23 and converts it into a digital signal; Receives the output of the ADC and performs digital signal processing to control the color displayed on the color display unit 30 to be changed according to the brain wave signal measured by the headset unit 10, thereby changing a specific color in response to a specific brain wave of the user. A digital signal processor (DSP) 25 which controls to extract the digital signal; A memory (26), connected to said DSP (25), for storing data related to neurofeedback processing; And a signal transmitter 27 connected to the DSP 25 and transferring the control command of the DSP 25 to the color display unit 30 via wire or wirelessly.

The neurofeedback apparatus using colors according to an embodiment of the present invention, as shown in Figure 7, LPF 21 for low-pass filtering the signal transmitted from the headset unit 10 via a wired or wireless; A notch filter (22) for notch filtering the signal output from the LPF (21); An amplifier 23 for amplifying the output of the notch filter 22; An ADC 24 which receives the analog output of the amplifier 23 and converts it into a digital signal; Receives the output of the ADC and performs digital signal processing to control the color displayed on the color display unit 30 to be changed according to the brain wave signal measured by the headset unit 10, thereby changing a specific color in response to a specific brain wave of the user. DSP 25 for controlling to extract the; A memory (26), connected to said DSP (25), for storing data related to neurofeedback processing; It is connected to the DSP 25, the signal transmission unit 27 for transmitting the control command of the DSP 25 to the color display unit 30 via a wired or wireless; comprises a neurofeedback processing unit 20, including It is done.

The neurofeedback processing unit 20 may further include a result display unit 28 displaying a result processed by the DSP 25.

FIG. 10 is a detailed block diagram of the color display unit of FIG. 1.

The color display unit 30 includes: a signal receiving unit 31 connected to the neuro feedback processing unit 20 by wire or wirelessly to receive a control command of the neuro feedback processing unit 20; A color display driver 32 for driving the color display 33 according to the control command received from the signal receiving section 31; And a color indicator 33 which is driven by the driving of the color display driver 32 to display colors.

As shown in FIG. 10, the neurofeedback apparatus using colors according to an embodiment of the present invention is connected to the neurofeedback processing unit 20 by wire or wirelessly to receive a control command of the neurofeedback processing unit 20. A signal receiver 31; A color display driver 32 for driving the color display 33 according to the control command received from the signal receiving section 31; And a color display unit 30, which is driven by the driving of the color display driver 32 to display colors.

11 is a flowchart illustrating a control method of a neurofeedback device using colors according to an embodiment of the present invention.

As shown in the drawing, the neurofeedback processing unit 20 receives a user's brain wave signal measured by the headset unit 10 and controls the color display unit 30 to output a reference color (ST1, ST2). )Wow; After the first step, the neurofeedback processing unit 20 receives the brain waves measured by the headset unit 10 to perform real-time brain wave measurement, and performs a color change (RGB change) by the EEG analysis data, and thus the color is constant. A second step (ST3 to ST5) for determining whether to converge; And a third step (ST6) of diagnosing the corresponding color as a color of a specific brain wave of the user when the colors output from the color display unit 30 converge regularly in the second step.

The first step is characterized in that the reference color is output in gray (Gray).

12 and 13 are flowcharts illustrating a control method in a reference mode of a neurofeedback device using colors according to an embodiment of the present invention.

As shown therein, an eleventh step ST11 of initializing the color display unit 30 for setting the reference mode of the neurofeedback device; A twelfth step (ST12 to ST17) of measuring the brain waves after the eleventh step and calculating and mapping 3D vector PSD (Power Spectral Density) to colors; After the twelfth step, the 3D vector sPSD (subtraction PSD) is calculated to map the sPSD and the RGB vector of the 3D vector to store the change value of the RGB vector, and then determine whether a predetermined time has elapsed ( ST18 to ST21); After a predetermined time has elapsed in the thirteenth step, a fourteenth step of classifying the subject's preferred color, determining the subject's preferred color, and performing a neurofeedback mode by loading a change value of the RGB vector (ST22 to ST25) Wow; And a fifteenth step (ST26 to ST32) of measuring the EEG after the fourteenth step to change the color of the color display unit 33.

In the eleventh step ST11, the initial color of the color display unit 30 is set to gray (R = 128, G = 128, B = 128), and the color signal step is set to 256 steps. .

The twelfth step ST12 to ST17 may include an electroencephalogram measurement step ST12 for measuring brain waves of a user after the eleventh step; An EEG signal preprocessing step (ST13) for performing an EEG signal preprocessing after the EEG measurement step (ST12); An EEG signal digitizing step (ST14) for performing EEG signal digitizing after the EEG signal preprocessing step (ST13); An ICA algorithm applying step (ST15) of applying an ICA (Independent Component Analysis) algorithm after the EEG signal digitizing step (ST14); A 3D vector PSD calculation step (ST16) of calculating a 3D vector PSD after the step of applying the ICA algorithm (ST15); And a color mapping step (ST17) for mapping the color with the 3D vector PSD after the 3D vector PSD calculation step (ST16).

In the EEG measurement step ST12, the measurement channel is set to Fp1, Fp2, AF3, and AF8, the drift protrusion is set as the reference channel, the Fpz is set as the ground channel, IA (Isolation amplify) amplification and ultra-short gain amplification. It characterized in that to perform.

The EEG signal preprocessing step ST13 may include performing low pass filtering (LPF), performing notch filtering (for example, 60 Hz / 50 Hz), and performing terminal gain amplification.

The EEG digitizing step (ST14) is characterized in that digitizing is performed under conditions including a sampling rate (for example, 250 Hz), a resolution (12 bits), and the number of channels (4CH, 4 channels).

The step of applying the ICA algorithm (ST15), performing digital filtering, one or more of Eye Blink (Artifact), EOG (Electro Oculography) artifacts, EMG (Electromyogram, EMG) artifacts It is characterized by removing artifacts.

The 3D vector PSD calculation step (ST16) is performed by applying a Fast Fourier Transform (FFT) / AR (Auto Regressive) technique, and sets a frequency resolution in advance (for example, 0.5 Hz). ), Apply a sliding technique (e.g. 75% overlap), set frequency bands for theta waves, alpha waves and beta waves (e.g., theta waves range from 4.0 to 7.0 Hz, the alpha waves range from 7.5 to 12.0 Hz, and the beta waves 16.0 to 22.0 Hz).

In the color mapping step (ST17), the theta waves are mapped to red, the alpha waves are mapped to green, and the beta waves are mapped to blue.

The thirteenth step (ST18 to ST21) may include: an sPSD calculation step (ST18) of performing a 3D vector sPSD calculation using the previous PSD and the current PSD after the twelfth step; An RGB vector mapping step (ST19) for mapping an RGB vector using a change amount of the 3D vector PSD after the sPSD calculation step; A storage step (ST20) of storing a change value of the RGB vector after the RGB vector mapping step; Determining whether a predetermined time has elapsed after the RGB vector mapping step, and if the predetermined time has not elapsed, returns to the sPSD calculation step, and if the predetermined time has elapsed, determining to return to the fourteenth step (ST21). It is characterized by performing.

The sPSD calculation step (ST18) is characterized by calculating the 3D vector sPSD by performing (previous PSD-current PSD) / (previous PSD) X 100%.

In the RGB vector mapping step ST19, RGB per 1% of the amount of change of the 3D vector PSD is mapped into a three-step change.

The fourteenth step ST22 to ST25 may include a loading step of loading a change value of the RGB vector stored in the thirteenth step; A color classification step (ST23) of classifying a subject's preferred color by using the change value of the RGB vector loaded in the loading step after the thirteenth step; A color determination step (ST24) of determining a subject's preferred color after the color classification step; And a mode selection step (ST25) for selecting a neurofeedback mode after the color determination step.

In the color classification step ST23, K-means clustering is applied, the number of clusters is determined, and a threshold value is selected to classify a subject's preferred color.

The color determination step (ST24), characterized in that the color corresponding to the average RGB value of the major cluster (Major cluster) that is the cluster with the most value of the vector is characterized in that the preferred color.

The mode selection step ST25 may include selecting a neurofeedback mode including one or more modes of relaxation, concentration, and depression improvement mode, and corresponding the EEG parameters to improve the preferred color.

The fifteenth step ST26 to ST32 may include an electroencephalogram measurement step ST26 for measuring brain waves of a user after the fourteenth step; An EEG signal preprocessing step (ST27) for performing an EEG signal preprocessing after the EEG measurement step (ST26); An EEG signal digitizing step (ST28) for performing EEG signal digitizing after the EEG signal preprocessing step (ST27); An ICA algorithm applying step (ST29) for applying an ICA algorithm after the EG digitizing step (ST28); A 3D vector PSD calculation step (ST30) for calculating a 3D vector PSD after the step of applying the ICA algorithm (ST29); A color changing step (ST31) of changing the color of the color indicator (33) after the 3D vector PSD calculation step (ST30); After the color change step ST31, it is determined whether there is no color change for a predetermined time, and if there is a change in color for a predetermined time, the step 15 is repeated. It is characterized by performing, including.

14 and 15 are flowcharts illustrating a control method in a neurofeedback mode of a neurofeedback device using colors according to an embodiment of the present invention, and FIGS. 16 and 17 are outputs of the neurofeedback mode in FIGS. 14 and 15. The figure shows an example.

As shown in FIG. 21, when the power is turned on by connecting to the neurofeedback device, a twenty-first step (ST41, ST42) is provided to allow a function selection; If the function selection is a training mode in the twenty-first step, a twenty-second step (ST43 to ST69) to perform one or more of improvement training including relaxation training, concentration training, or depression training; If the function selection is the display mode in the twenty-first step, the twenty-third step (ST70 ~ ST79) to perform the EEG display or stored data display; characterized in that it comprises.

The twenty-second step ST43 to ST69 may include a function discrimination step ST43 for determining whether a user's function selection is a training mode; A relaxation mode determination step (ST44) of determining whether the user's function selection is a relaxation mode if the user's function selection is a training mode in the function discriminating step; A relaxation improvement training step (ST45 to ST52) for performing relaxation improvement training if the user's function selection in the relaxation mode determining step is a relaxation mode; A concentration mode determination step (ST53) of determining whether the user's function selection is an attention mode if the user's function selection is not a relaxation mode in the relaxation mode determination step; A concentration improvement training step (ST54 to ST61) in which the concentration improvement training is performed if the user's function selection is in the concentration mode in the concentration mode determination step; If the user's function selection in the concentration mode determination step is not the concentration mode, depression improvement training step (ST62 ~ ST69) to perform the depression improvement training, characterized in that it comprises a.

The relaxation improvement training step (ST45 ~ ST52), the concentration improvement training step (ST54 ~ ST61) and the depression improvement training step (ST62 ~ ST69), respectively, the training performing step (ST45, ST54, ST62) to perform the training Wow; A stop-on determination step (ST46, ST55, ST63) for determining whether a stop button is turned on during the execution of the training step; A storage discrimination step (ST47, ST56, ST64) for determining whether to store data when the stop button is turned on in the stop-on determination step; If it is determined that the data is to be stored in the storage discrimination step, storing data related to training in the memory 26 (ST48, ST57, ST65); If it is not determined that the data is to be stored in the storage discrimination step, ignore the steps (ST49, ST58, ST66); A display color invariant determination step (ST50, ST59, ST67) for determining whether the display color is invariant if the stop button is not turned on in the stop-on determination step; In the display color invariant determination step, if the display color is not invariant, return to the training execution step. If the display color is invariant, store the data at this time in the memory 26 and notify the result through the result display unit 28 (ST51). , ST52, ST60, ST61, ST68, ST69); characterized in that to perform.

The twenty-third step (ST70 ~ ST79), if the function selection of the display mode in the twenty-first step EEG display, EEG display step (ST70 ~ ST72) for displaying the real-time brain waves of the user; A storage data display step (ST73 to ST79) of displaying data stored in the memory 26 including at least one of a relative power density and a power density for each number of times if the function selection of the display mode is the storage data display in the twenty-first step; It characterized in that to perform including.

In the storage data display steps ST73 to ST79, the relative power density is displayed in a bar graph.

In the stored data display steps ST73 to ST79, the power density for each number of times is displayed as a broken line.

Referring to the accompanying drawings, the operation of the neurofeedback device using the color and the control method according to the present invention configured as described above in detail as follows.

First, the present invention aims to solve brain dysfunction by unconsciously extracting preferred colors from the brain.

The conventional BCI system has a problem in that it is necessary to maintain a mental state that continues to focus on other kinds of mental states having specific reflections in internal images or EEG patterns that the BCI can recognize well. In the conventional BCI system, external objects and movements require a player to play the piano or ride a bicycle while constantly thinking about how to move a finger or foot, for example. It is never easy to play the piano or ride a bicycle while thinking. Luckily, after constant training, almost anyone can automatically play piano music and bike without conscious control. This process is known as automation during the acquisition of motor skills. Several sets of brain regions are responsible for the behavior before and after automatic manipulation. Unfortunately, in BCI systems, where the best synchronization is achieved, automatic manipulation does not occur in humans after several years of training.

The main reason for the inadequate automatic manipulation process in this BCI system is the visible uncertainty of the EEG signal as a double uncertainty that exists between the object of conditioning (e.g. cursor movement on the screen) and the compensation as a conscious assessment of successful attempts. Can be.

The first of them is the absence of a clear decision between the user's conscious intention to obtain a clear mental state (image) and the result of this process. The second is a deficiency of a clear relationship with the EEG pattern obtained even with simpler images of motion, even with more complex images. As a result, the user cannot invoke the same EEG pattern each time he tries, and the BCI cannot properly recognize the user's intentions.

One solution to overcome this conventional problem is to remove conscious control of feedback information in the BCI system. This way of removing conscious control seems impossible because the user loses the positive reinforcement that comes after successful attempts at conscious evaluation. Indeed, in the classical structure of the NF, feedback gives the user an insignificant signal, such as moving the cursor on the screen, and the previous command allows the participant to turn this signal into a hardening reward.

Basically, unconscious reinforcement compensation is possible only if it is actively working on the feedback signal per session without prior knowledge. In this case, if the user can handle the BCI without conscious control, he will automatically try to keep the feedback signal to himself comfortably and eliminate unwanted signals.

Thus, the user will automatically select the mental state suitable for operating the BCI system. After the training procedure, the user will automatically use this unconscious mental state as brain unconscious commands for the BCI system. This is an important idea of a solution for developing a new paradigm for BCI systems.

Thus, the present invention allows the arbitrary conditioning of EEG patterns in humans at the subconscious level, and the present invention uses color as the information transfer medium in the BCI's feedback channel.

It is based on the idea that surrounding colors can change mental states in a clear direction at a subconscious level. This concept also presupposes that when a person chooses a favorite color background or color card, the person tends to be in a color state that unconsciously affects his mental state. This choice of color spectrum can be said to be a human emotional language acquired in childhood.

The compensation signal for color has an unconscious reinforcement effect in the NF paradigm, and the color-induced EEG pattern can be used as a handling signal for the BCI system. Hence, color compensation signals are used to track directional changes in dynamic EEG patterns during NF training. Accordingly, positive reinforcement by comfortable color leads to more frequent occurrence of the corresponding EEG pattern. Conversely, negative reinforcement by avoiding colors will lead to or even disappear of the corresponding EEG pattern.

The present invention describes the EEG pattern that occurs during the process of color regulation and stealth learning in the BCI paradigm. The present invention emphasizes the concept of a method of automatically operating BCI without conscious control and the possibility for expressing natural human emotion as it occurs when playing piano through BCI as a result.

To this end, in the present invention, as shown in FIG. 1, the headset unit 10, the neurofeedback processing unit 20, and the color display unit 30 may be configured as a whole, or may be configured separately.

Here, the headset 10 measures the brain waves of the user in response to the color generated by the color display unit 30. The headset unit 10 may be connected to the neuro feedback processing unit 20 by wire or wirelessly.

The headset unit 10 may be composed of a sensor 11, an isolation amplifier 12, an amplifier 13 as shown in FIG.

Therefore, the sensor 11 of the headset unit 10 measures an electroencephalogram of a user in response to the color generated by the color display unit 30, and may be configured as an Ag / Agcl sensor. In this case, in the international 10-20 system of FIG. 18, the measurement channel may be set to electrodes Fp1, Fp2, AF7, and AF8, and the reference state may be set to Fpz. Therefore, the measurement channels are Fp1, Fp2, AF7, and AF8, the reference channel is the yolk, and the ground channel is set to Fpz.

In addition, the isolation amplifier 12 of the headset unit 10 isolated and amplified the signal measured by the sensor (11).

In addition, the amplifying unit 13 of the headset unit 10 amplifies the isolation amplified signal from the isolation amplifying unit 12 and transmits it to the neurofeedback processing unit 20. At this time, the amplification unit 13 of the headset unit 10 performs ultra-short gain amplification, and the terminal gain amplification is performed by the amplifying unit 23 of the neurofeedback processing unit 20.

An example of the actual configuration of the headset unit 10 is illustrated in FIGS. 3 to 6.

In addition, the neurofeedback processing unit 20 is connected to the headset unit 10 by wire or wirelessly, and controls the color displayed on the color display unit 30 to be changed according to the EEG signal measured by the headset unit 10 so as to change the user's specificity. It extracts specific colors that respond to brain waves.

As shown in FIG. 7, the neurofeedback processing unit 20 includes an LPF 21, a notch filter 22, an amplifier 23, an ADC 24, a DSP 25, a memory 26, and a signal transmitter ( 27), and may further include a result display unit (28).

Therefore, the LPF 21 of the neurofeedback processing unit 20 performs low pass filtering on the signal transmitted from the headset unit 10 via wire or wirelessly and transmits the signal to the notch filter 22.

In addition, the notch filter 22 of the neurofeedback processing unit 20 notch filters the signal output from the LPF 21. Here, notch filtering refers to filtering that exhibits a sharp attenuation characteristic of a sharp side of a specific frequency response curve.

In addition, the amplifier 23 of the neurofeedback processing unit 20 amplifies the output of the notch filter 22. The amplifier 23 of the neurofeedback processing unit 20 performs terminal gain amplification compared with the amplifier 13 of the headset unit 10.

In addition, the ADC 24 of the neurofeedback processor 20 receives an analog signal output from the amplifier 23, converts the analog signal into a digital signal, and delivers the analog signal to the DSP 25.

In addition, the DSP 25 of the neurofeedback processing unit 20 receives the digital signal output from the ADC 24 and performs digital signal processing to display it on the color display unit 30 according to the EEG signal measured by the headset unit 10. By controlling the color to be changed to control to extract a specific color in response to the user's specific brain waves. By the control in the DSP 25, the operation of the reference mode and the neurofeedback mode is performed.

In addition, the memory 26 of the neurofeedback processing unit 20 is connected to the DSP 25 and stores data related to the neurofeedback processing.

In addition, the signal transmission unit 27 of the neurofeedback processing unit 20 is connected to the DSP 25 and transmits a control command of the DSP 25 to the color display unit 30 via wire or wireless.

In addition, the result display unit 28 of the neurofeedback processing unit 20 displays the result processed by the DSP 25.

The neurofeedback processing unit 20 may be configured in the shape as shown in FIGS. 8 and 9.

So, in front of the neurofeedback processing unit 20, as shown in Fig. 8, the input jack, the antenna, the liquid crystal display (LCD), the start button, the move button, the stop button, the OK button, A lamp, a power on / off button, an output jack, and the like can be seen.

Also, as shown in FIG. 9, an antenna, an input jack, a speaker, a belt clip, a battery holder, an output jack, and the like may be seen from the back of the neurofeedback processing unit 20.

The color display unit 30 is connected to the neurofeedback processing unit 20 by wire or wirelessly, and changes the color according to the control of the neurofeedback processing unit 20 to display the color.

The color display unit 30 may be constituted by the signal receiver 31, the color display driver 32, and the color display 33 as shown in FIG.

Thus, the signal receiving unit 31 of the color display unit 30 is connected to the neuro feedback processing unit 20 by wire or wirelessly and receives the control command of the neuro feedback processing unit 20 and transmits it to the color display driver 32.

In addition, the color display driver 32 of the color display unit 30 drives the color display 33 in accordance with a control command of the DSP 25 of the neurofeedback processing unit 20 received from the signal receiving unit 31.

In addition, the color display 33 of the color display unit 30 is driven by the driving of the color display driver 32 to display the color. Then, the user's response to the color display 33 is sensed by the Ag / Agcl sensor 11 through the headset unit 10 mounted on the user's head, and the neurofeedback processing unit 20 may process the reaction.

Meanwhile, the control method of the present invention can be performed as shown in FIG.

Therefore, first, the Ag / Agcl sensor 11 of the headset unit 10 measures the EEG of the user in response to the color indicator 33 of the color display unit 30 (ST1).

Then, the neurofeedback processing unit 20 receives an EEG signal of the user measured by the headset unit 10 and controls the color display unit 30 to output a reference color (ST2). At this time, the reference color may be output in gray.

The neurofeedback processing unit 20 receives the brain waves measured by the headset unit 10 and performs real-time brain wave measurement (ST3).

In addition, the color output from the color display unit 30 is controlled to change according to the measured EEG to perform a color change (RGB change) by EEG analysis data (ST4).

Thus, it is determined whether the colors constantly converge (ST5).

Real-time EEG measurement and RGB changes are repeatedly performed until the colors converge constantly, and when the colors converge constantly, the color is diagnosed as the color of the specific EEG of the user (ST6).

12 and 13 are flowcharts illustrating a control method in a reference mode of a neurofeedback device using colors according to an embodiment of the present invention. The operation in the reference mode will be described in detail as follows.

First, in the eleventh step ST11, the color display unit 30 is initialized to set the reference mode of the neurofeedback device. At this time, the initial color of the color display unit 30 may be set to gray (R = 128, G = 128, B = 128), and the color signal step may be set to 256 steps (ST11).

In the twelfth step ST12 to ST17, the brain wave is measured to calculate and map the 3D vector PSD to color.

To this end, the EEG measurement is first performed by the Ag / Agcl sensor 11, which sets the measurement channels to Fp1, Fp2, AF3, and AF8, sets the guide projection to the reference channel, and sets Fpz to the ground channel. In this case, the electrodes of the Ag / Agcl sensor 11 may be set to be positioned at about 1.5 cm above and below the left and right eye tail and the left eye. In addition, IA amplification by the isolation amplifier 12 and ultra-short gain amplification by the amplifier 13 are performed (ST12).

And pre-processing the EEG signal. It performs low pass filtering (LPF) in the LPF 21, notch filtering (eg, 60 Hz / 50 Hz) in the notch filter 22, and terminates gain amplification in the amplifier 23 ( ST13).

Then, EEG signal digitization is performed. This is performed in the DSP 25 using the digital signal converted in the ADC 24. Thus, digitizing is performed under the conditions including the sampling rate (for example, 250 Hz), the resolution (12 bits), and the number of channels (4CH, 4 channels) (ST14).

And apply ICA algorithm. That is, digital filtering is performed and eye blink artifacts, EOG artifacts, and EMG artifacts are removed (ST15).

Also calculate 3D vector PSD. This is done by applying the FFT / AR technique. The frequency resolution can be set to 0.5Hz, with a sliding technique and set to 75% redundancy. And the frequency band is set for theta wave, alpha wave and beta wave, for example, theta wave can be set to 4.0 ~ 7.0Hz, alpha wave 7.5 ~ 12.0Hz, beta wave 16.0 ~ 22.0Hz (ST16).

And map the color with the 3D vector PSD. To do this, for example, theta waves may be mapped to red, alpha waves to green, and beta waves to blue (ST17).

In the thirteenth step (ST18 to ST21), the 3D vector sPSD, which is the change amount of the 3D vector, is calculated, the sPSD and the RGB vector of the 3D vector are mapped to store the change value of the RGB vector, and then it is determined whether a predetermined time elapses.

So first we perform the sPSD calculation. For example, 3D vector sPSD may be calculated by performing (old PSD-current PSD) / (old PSD) X 100% (ST18).

Then map the RGB vectors. For example, RGB per 1% of the amount of change in the 3D vector PSD may be mapped to a three-step change. The color is displayed on the color display 33 of the color display unit 30 using the mapped result (ST19).

Then, the change value of the RGB vector is stored (ST20).

After mapping the RGB vectors, it is determined whether a certain time has elapsed.

If the predetermined time has not elapsed, the process returns to the sPSD calculation step and re-performs the sPSD calculation and the RGB vector mapping. If the predetermined time has elapsed, the process returns to the next step 14 (ST21).

In addition, in the fourteenth step (ST22 to ST25), the change of the RGB vector is loaded to classify the subject's preferred color, determine the subject's preferred color, and perform neurofeedback mode selection.

Therefore, first, the change value of the RGB vector stored in the thirteenth step is loaded (ST22).

The subject's preferred color is classified by using the change value of the loaded RGB vector. As a result, individual preference colors are determined through clustering analysis of the variation amount (sPSD) of the stored 3D vector. The subject's preferred color classification may apply K-means clustering or PCA (Prime Component Analysis). Here, K-means clustering (K-means clustering) is a technique that clusters the elements located close to each other based on the assumption that elements with similar characteristics are located close to each other, and PCA performs clustering through principal component analysis. to be. Therefore, when using K-means clustering, the number of clusters is determined, and a threshold value is selected. In this case, the threshold value may be selected using a method of excluding a distance deviation of 36.4 or less (corresponding to 7% variation of PSD) in the RGB 3D space from the classification (ST23).

It also determines the subject's preferred color. This may determine the color corresponding to the average RGB value of the major cluster, which is the cluster having the largest value of the vector, as the preferred color (ST24).

Then select the neurofeedback mode. It may select a neurofeedback mode, including one or more modes among relaxation, concentration, and depression improvement modes. In addition, it may be mapped to the EEG parameter to improve the preferred color (ST25).

In addition, in the fifteenth step ST26 to ST32, the color of the color indicator 33 is changed by measuring the EEG.

The EEG measurement step (ST26), EEG signal preprocessing step (ST27), EEG signal digitizing step (ST28), ICA algorithm application step (ST29), 3D vector PSD calculation step (ST30) is the EEG measurement step (12) ST12), the EEG signal preprocessing step ST13, the EEG signal digitizing step ST14, the ICA algorithm application step ST15, and the 3D vector PSD calculation step ST16.

Then, the color of the color indicator 33 is changed (ST31).

Thus, it is determined whether there is no color change for a predetermined time, and if there is a color change for a predetermined time, the fifteenth step is performed again, and if there is no color change for a predetermined time, the process ends (ST32).

14 and 15 are flowcharts illustrating a control method in a neurofeedback mode of a neurofeedback device using colors according to an embodiment of the present invention. This will be described in detail the operation of the neurofeedback mode as follows.

First, when the neurofeedback device is powered on (ST41), the neurofeedback device 20 outputs a screen for selecting a function in the neurofeedback processing unit 20 (ST42).

Then, a screen for selecting a training mode is output (ST43).

At this time, when the user selects the training mode, a screen for selecting the relaxation mode is output (ST44).

Thus, when the user selects the relaxation mode, relaxation improvement training is performed (ST45).

When the stop button of the neurofeedback processing unit 20 is turned on while the relaxation improvement training is performed (ST46). A screen asking whether to save data is output (ST47). If the user selects to save the data, the result data of the relaxation improvement training is stored in the memory 26 of the neurofeedback processing unit 20 (ST48). If the user does not select data storage, it is ignored (ST49).

If the stop button of the neurofeedback processing unit 20 is not turned on while the relaxation improvement training is performed, it is determined whether the color displayed on the color display unit 30 is invariant (ST50). Therefore, if the display color is not constant, the relaxation training is continued again. If the display color is invariant, the result data at this time is stored in the memory 26 of the neurofeedback processing unit 20 (ST51), and the user is notified (ST52).

In addition, if the user does not select the relaxation mode, a screen for selecting the concentration mode is output (ST53).

Thus, when the user selects the concentration mode, concentration improvement training is performed (ST54).

When the stop button of the neurofeedback processing unit 20 is turned on while the concentration improvement training is performed (ST55). A screen asking whether to save data is output (ST56). When the user selects to save the data, the result data of the concentration improvement training is stored in the memory 26 of the neurofeedback processing unit 20 (ST57). If the user does not select data storage, it is ignored (ST58).

If the stop button of the neurofeedback processing unit 20 is not turned on while the concentration improvement training is performed, it is determined whether the color displayed on the color display unit 30 is invariant (ST59). Therefore, if the display color is not changed, the concentration improvement training can be continued again. If the display color is invariant, the result data at this time is stored in the memory 26 of the neurofeedback processing unit 20 (ST60), and the user is notified (ST61).

In addition, if the user does not select the concentration mode, the depression improvement training is performed (ST62).

When the stop button of the neurofeedback processing unit 20 is turned on while the depression improvement training is performed (ST63). A screen asking whether to save data is output (ST64). When the user selects to save the data, the result data of the depression improvement training is stored in the memory 26 of the neurofeedback processing unit 20 (ST65). If the user does not select data storage, it is ignored (ST66).

If the stop button of the neurofeedback processing unit 20 is not turned on during the depression training, the color displayed on the color display unit 30 is determined to be invariant (ST67). Therefore, if the display color is not constant, depression training can be continued again. If the display color is invariant, the result data at this time is stored in the memory 26 of the neurofeedback processing unit 20 (ST68), and the user is notified (ST69).

In addition, if the user does not select a training mode, a screen for selecting whether to display the EEG is output (ST70).

Thus, when the user selects the EEG display, real-time EEG display is performed (ST71). If the OK button of the neurofeedback processing unit 20 is not pressed, real-time EEG display is continuously performed, and when the OK button of the neurofeedback processing unit 20 is pressed, the initial function selection mode is output (ST42).

If the user does not select the EEG display, the stored data display is output (ST73).

Thus, if the user does not select the display of stored data, the initial function selection mode is output (ST42), and if the user selects the display of stored data, a screen for selecting whether to output the relative power density is output (ST74).

Thus, when the user selects to output the relative power density, the relative power density is displayed in the bar graph (ST75). If the OK button of the neurofeedback processing unit 20 is not pressed, the bar graph display for the relative power density is continuously performed, and if the OK button of the neurofeedback processing unit 20 is pressed, the initial function selection mode is output (ST42). ).

If the user does not select the relative power density, a screen for selecting whether to output the power density for each time is output (ST77).

Thus, when the user selects to output the power density for each time, the power density for each time is displayed as a broken line (ST78). If the OK button of the neurofeedback processing unit 20 is not pressed, the display of the broken line for the power density for each time is continuously performed, and if the OK button of the neurofeedback processing unit 20 is pressed, the initial function selection mode is output ( ST42).

16 and 17 show examples of output of the neurofeedback mode in FIGS. 14 and 15.

16A illustrates a training mode in a training mode, a brainwave display, and a stored data display on an initial screen of the result display unit 28 of the neurofeedback processing unit 20. Here is an example. (B) of FIG. 16 shows an example of outputting relaxation training, attention training, and anti-depression training mode when the training mode is selected in (a). It is seen.

In addition, Figure 16 (c) shows an example of selecting a brainwave display (Brainwave Display) in (a). (D) of FIG. 16 shows an example in which the brain waves of the right hemisphere and the left hemisphere are output when the brain wave display is selected in (c).

In addition, FIG. 16E illustrates an example in which a stored data display is selected in (a). FIG. 16 (f) shows an example of outputting a relative power density and a trail result history mode when the stored data display is selected in (e).

In addition, FIG. 17G illustrates an example in which a relative power density is selected in FIG. 16F. FIG. 17H illustrates an example in which a relative power density of the right brain and the left brain is displayed in a bar graph in FIG. 17G.

17 (i) shows an example of selecting a trail result history by the number of times in FIG. 16 (f). FIG. 17 (j) shows an example in which power densities for the right brain and the left brain are indicated by broken lines in FIG. 17 (i).

As described above, the present invention solves brain dysfunction by unconsciously extracting preferred colors from the brain.

As described above, the neurofeedback device using the color according to the present invention and a control method thereof have the effect of unconsciously extracting preferred colors from the brain to solve brain dysfunction.

Thus, the present invention has the effect of providing a tool that allows users to handle their cognitive and emotional means with optimal automatic self-regulation through the optimization of unconscious BCI training using color.

Although the above has been described as being limited to the preferred embodiment of the present invention, the present invention is not limited thereto and various changes, modifications, and equivalents may be used. Therefore, the present invention can be applied by appropriately modifying the above embodiments, it will be obvious that such application also belongs to the scope of the present invention based on the technical idea described in the claims below.

Claims (35)

delete delete delete delete delete delete delete delete delete delete A neurofeedback processor comprising: a first step of receiving an EEG signal of the user measured by the headset unit and controlling a color display unit to output a reference color; A second step of determining, after the first step, the neurofeedback processing unit receives the brain waves measured by the headset unit to perform real-time EEG measurement, and performs color change by EEG analysis data to determine whether the colors constantly converge; And a third step of diagnosing the corresponding color as a color of a specific brain wave of the user when the colors output from the color display unit are uniformly converged in the second step of the neurofeedback apparatus. Control method. The method of claim 11, wherein the first step, A control method of a neurofeedback device using a color, characterized in that the reference color is output in gray. An eleventh step of initializing a color display unit for setting a reference mode of the neurofeedback device; A twelfth step of measuring the brain waves after the eleventh step and calculating and mapping the 3D vector PSD to color; A thirteenth step after calculating the 3D vector sPSD, mapping the sPSD and the RGB vector of the 3D vector to store a change value of the RGB vector, and then determining whether a predetermined time has elapsed; A 14th step of classifying the subject's preferred color, determining the subject's preferred color, and performing a neurofeedback mode by loading a change value of the RGB vector after a predetermined time has elapsed in the thirteenth step; And a fifteenth step of changing the color of the color display by measuring the EEG after the fourteenth step. The method of claim 13, wherein the eleventh step, The initial color of the color display unit is set to gray, the color signal control step of the neurofeedback device using the color, characterized in that the setting in 256 steps. The method of claim 13, wherein the twelfth step, An EEG measurement step of measuring an EEG of the user after the eleventh step; An EEG signal preprocessing step of performing an EEG signal preprocessing after the EEG measurement step; An EEG signal digitizing step of performing EEG signal digitizing after the EEG signal preprocessing step; An ICA algorithm applying step of applying an ICA algorithm after the EEG signal digitizing step; A 3D vector PSD calculation step of calculating a 3D vector PSD after the step of applying the ICA algorithm; And a color mapping step of mapping the 3D vector PSD and the color after the 3D vector PSD calculation step. The method of claim 15, wherein the EEG step, Color feedback neurofeedback device characterized in that the measurement channel is set to Fp1, Fp2, AF3, AF8, the flotation projection as the reference channel, the Fpz as the ground channel, and performs IA amplification and ultra-short gain amplification Control method. The method according to claim 15, wherein the EEG signal preprocessing step, A method for controlling a neurofeedback using color, characterized in that it performs lowpass filtering, notch filtering, and terminal gain amplification. The method of claim 15, wherein the EEG digitizing step, A method for controlling a neurofeedback using color, characterized in that digitizing is performed under conditions including sampling rate, resolution, and number of channels. The method of claim 15, wherein applying the ICA algorithm, A method of controlling a neurofeedback device using color, which performs digital filtering and removes one or more artifacts from eye blink artifacts, EOG artifacts, and EMG artifacts. The method of claim 15, wherein the 3D vector PSD calculation step, A method of controlling a neurofeedback using color, which is performed by applying an FFT / AR technique, presets a frequency resolution, applies a sliding technique, and sets frequency bands for theta waves, alpha waves, and beta waves. The method of claim 15, wherein the color mapping step, Theta waves are mapped to red, alpha waves are mapped to green, and beta waves are mapped to blue. The method according to claim 13, wherein the thirteenth step, An sPSD calculation step of performing a 3D vector sPSD calculation using the previous PSD and the current PSD after the twelfth step; An RGB vector mapping step of mapping an RGB vector using a change amount of a 3D vector PSD after the sPSD calculation step; A storage step of storing a change value of the RGB vector after the RGB vector mapping step; Determining whether a predetermined time has elapsed after the RGB vector mapping step, and if the predetermined time has not elapsed, returning to the sPSD calculation step, and determining whether to return to the fourteenth step if the predetermined time has elapsed; Control method of a neurofeedback device using the color, characterized in that. The method of claim 22, wherein the sPSD calculation step, A method for controlling a neurofeedback using color, characterized in that calculating the 3D vector sPSD by performing (formerly PSD-current PSD) / (formerly PSD) x 100%. The method of claim 22, wherein the RGB vector mapping step, A method for controlling a neurofeedback using color, characterized in that the RGB per 1% of the change amount of the 3D vector PSD is mapped into three steps. The method of claim 13, wherein the fourteenth step, Loading a change value of the RGB vector stored in the thirteenth step; A color classification step of classifying a subject's preferred color by using the change value of the RGB vector loaded in the loading step after the thirteenth step; A color determination step of determining a subject's preferred color after the color classification step; And a mode selection step of selecting a neurofeedback mode after the color determination step. The method of claim 25, wherein the color classification step, A method for controlling neurofeedback using color, characterized by applying K-means clustering, determining the number of clusters, and selecting threshold values to classify subject's preferred colors. The method of claim 25, wherein the color determination step, The method of controlling a neurofeedback using color, characterized in that the color corresponding to the average RGB value of the major cluster, which is the cluster with the largest value of the vector, is determined as the preferred color. The method of claim 25, wherein the mode selection step, A method of controlling a neurofeedback device using color, characterized in that the neurofeedback mode is selected, including one or more modes of relaxation, concentration, and depression improvement, and matched with an EEG parameter to improve a preferred color. The method according to claim 13, wherein the fifteenth step, An EEG measurement step of measuring the EEG of the user after the fourteenth step; An EEG signal preprocessing step of performing the EEG signal preprocessing after the EEG measurement step; An EEG signal digitizing step of performing EEG signal digitizing after the EEG signal preprocessing step; An ICA algorithm applying step of applying an ICA algorithm after the EEG signal digitizing step; A 3D vector PSD calculation step of calculating a 3D vector PSD after the step of applying the ICA algorithm; A color change step of changing a color of the color indicator after the 3D vector PSD calculation step; And determining whether there is no change in color for a predetermined time after the color change step, performing the fifteenth step again if there is a change in color for a predetermined time, and ending if there is no change in color for a predetermined time; Control method of a neurofeedback device using the color, characterized in that. A twenty-first step of enabling a function selection when a neurofeedback device is connected and powered on; In a twenty-first step, if the function selection is a training mode, a twenty-second step of performing one or more of improvement training including relaxation training, concentration training, or depression training; And a twenty-third step of performing EEG display or stored data display if the function selection is the display mode in the twenty-first step; and performing the control of the neurofeedback device using color. The method of claim 30, wherein the twenty-second step, A function discrimination step of determining whether the user's function selection is a training mode; A relaxation mode determination step of determining whether the user's function selection is a relaxation mode if the user's function selection is a training mode in the function determination step; A relaxation improvement training step of performing relaxation improvement training if the user's function selection in the relaxation mode determining step is a relaxation mode; A concentration mode determination step of determining whether the user's function selection is a concentration mode if the user's function selection is not a relaxation mode in the relaxation mode determination step; A concentration improvement training step of performing concentration concentration training if the function selection of the user is in the concentration mode in the concentration mode determination step; If the user's function selection in the concentration mode determination step is not the concentration mode, depression improvement training step to perform the depression improvement training; control method of the neurofeedback apparatus using color, characterized in that performed. The method of claim 31, wherein the relaxation improvement training step, the concentration improvement training step and the depression improvement training step, A training step of performing training; A stop-on determining step of determining whether the stop button is turned on during the performing of the training step; A storage discrimination step of determining whether to store data when the stop button is turned on in the stop-on determination step; If it is determined that the data is to be stored in the storage discrimination step, storing data related to training in a memory; If it is not determined that the data is to be stored in the storage discriminating step, ignoring it; A display color invariant determination step of determining whether the display color is invariant if the stop button is not turned on in the stop on determination step; And if the display color is not invariant in the display color invariant determination step, returning to the training execution step, and if the display color is invariant, storing the data in the memory and notifying the result through the result display unit. Control method of a neurofeedback device using the color. The method of claim 30, wherein the twenty-third step, An EEG display step of displaying real-time EEG of the user if the function selection of the display mode is EEG display in the twenty-first step; If the function selection of the display mode is storing data display in the twenty-first step, storing data display step of displaying data stored in the memory including at least one of a relative power density and a power density for each number of times; Control method of a neurofeedback device using the color. The method of claim 33, wherein the relative power density in the stored data display step, Control method of a neurofeedback device using the color, characterized in that displayed in a bar graph. The method of claim 33, wherein the power density for each number of times in the stored data display step, A control method of a neurofeedback device using color, characterized in that displayed in a broken line.

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