KR20030002677A - Wireless telemetric system and method for neurofeedback training using parameters of electroencephalogram(EEG) - Google Patents

Abstract

The present invention relates to a neurofeedback training wireless system and a method for training an individual by changing the characteristics of EEG according to EEG activity by providing information about continuously changing EEG activity levels in the form of visual and auditory stimuli. will be.

Neurofeedback system according to the present invention is a brain signal detection unit for extracting a bioelectrical signal from the scalp through a headset with an electrode, a brain signal for wirelessly transmitting the signal by amplifying, filtering, and digitally converting the signal detected from the headset Analyze the individual's psychophysiological status by analyzing the serial signal received from the data radio transmitter, the brain signal data radio receiver that receives the transmitted signal wirelessly and performs serial transmission and reception with the neurofeedback execution unit, and the brain signal data receiver. It consists of a Neurofeedback Execution Unit that allows you to do Neurofeedback training in the form of a game.

In addition, the method of the present invention extracts the bioelectrical signals (EEG, EOG, EMG) from a headset equipped with a series of electrodes intended to be used in the head, and the signal is remotely connected to the brain signal data wireless receiver via a serial port. Wirelessly transmits to a host computer located at the PC, displays information about the EEG current activity level in the form of graphs, figures, images and audio signals on the monitor, allowing the individual to include training sessions and protocols set up in the form of computer games. Conduct neurofeedback training.

Description

Wireless system and method for neurofeedback training using EEG parameters {wireless telemetric system and method for neurofeedback training using parameters of electroencephalogram (EEG)}

The present invention seeks to provide a wireless system and feedback method for neurofeedback training to change a user's mental state, including focus attention or sustained attention, based on the spectral characteristics of EEG.

In addition, to achieve the above object by developing a wireless RF EEG transmission system applying a spread spectrum wireless transmission and reception technology that transmits light, convenient and reliable EEG signals to remove the inconvenience that the conventional user felt due to the wire, while using the range We also want to expand it.

FIELD OF THE INVENTION The present invention relates to wireless systems and methods for neurofeedback training, and in particular, the present invention relates to relaxation or attention using training designed with general neurofeedback training protocols or computer games controlled by parameters of EEG activity. A radio telemetric system and method for performing neurofeedback based on parameters derived by real-time spectral analysis of brain waves for inducing states or improving performance. .

The present invention may relate to three subject areas: 1) biofeedback (especially electroencephalogram (EEG) biofeedback, or neurofeedback), 2) brain waves associated with mental states (hereinafter referred to as EEG), in particular human operators. Neurofeedback procedures applicable to attention enhancement or cognitive functioning in patients with attention-related disorders that may be applied to performance, and 3) RF radio transmission of EEG signals for real-time monitoring and neurofeedback systems.

Biofeedback shows humans one or more physiological activities that were not previously perceived in real time, and trains them to use these physiological feedback information to change physiological activities in the intended direction and reach the required change targets. It is a technique that can induce a desirable psychophysiological state by rewarding time. Biofeedback training is similar to the operational conditioning used in physiological psychology and applied physiology. The biofeedback technique may consist of the presentation of selected target parameters (eg, heart rate) measured in real time for the acquisition and processing of any selected physiological activity (eg, electrocardiogram). When the target parameter reaches a preselected threshold level, the user receives a feedback signal as a reward for it. For example, if your heart rate drops from 75 to 70 during your heart rate biofeedback, and your training goal is set to a training threshold that reduces your heart rate to 70 per minute, you may receive an auditory signal as an immediate reward. .

Neurofeedback means using only EEG as a user's physiological activity. That is, selected EEG activity, such as the spectral power of a particular bandwidth obtained by Fast Fourier Transform (FFT) (hereinafter referred to as FFT) and the like, is used as the target for biofeedback. EEG is bioelectric activity produced by the brain (peak amplitude of 1-100 μV in the frequency range of about 0.5 to 42 Hz) and its activity signal is typically recorded by a sensor attached to the scalp. More precisely, several EEG electrodes, or sensors, are attached to standardized locations on the scalp to measure the continuous, spontaneous induced bioelectric activity produced by the internal cortex. Many cognitive and other mental processes (eg, attention) that occur in the cortex are associated with specific bioelectric cortical activity, and brain activation status can also be assessed by these EEG parameters. There are various EEG amplitude and frequency parameters that characterize different cognitive and mental states, and FFT analysis is one of the most common methods for evaluating EEG rhythm activity parameters of defined amplitude.

In general, four main EEG bands are presented: beta rhythms (13.0-40.0 Hz), which usually occur when eyes are open, focused or alert; Alpha rhythm (8.0-12.0 Hz), which is more relaxed with eyes closed and occurs mainly when the boundaries are slowed down; Theta rhythms (4.0-8.0 Hz) that occur predominantly in conditions such as daydreaming characterized by slower EEG activity; Delta rhythm (0.5-4.0 Hz) associated with sleep and sometimes pathological brain activity. In addition, special EEG rhythms have been reported, such as Sensorymotor Rhythm (SMR, 12.0-15.0 Hz) (hereinafter referred to as SMR). Scientist Sterman et al. (1974) are known to have linked this rhythm to epileptic seizures.

SMR is also associated with the inhibition of the motor process. However, in modern psychophysiology, narrower band rhythms, such as slow alpha (8.0-10.0 Hz) and fast alpha (10.0-13.0 Hz), slow beta (13.0-20.0 Hz) and fast beta (20.0-35.0 Hz), High frequency band (eg gamma, 36.0-42.0 Hz) is used. To date, it is known that narrow EEG bands of activity based on 1 Hz are of some functional importance in themselves and can be more accurately correlated with mental states.

The ability to control mental state is known to be gained by neurofeedback training. Many different approaches have been applied to EEG biofeedback to achieve mental state control.

U.S. Patent 5,406,957 describes a device and method for monitoring, analyzing and using EEG data, while another U.S. Patent 4,928,704 describes a training device and method for developing an effective voluntary control of EEG activity. An EEG sensor is attached to the cortical areas of the head to extract the EEG signal in a controlled laboratory environment, whereby the extracted signals are amplified and filtered by the predefined lower frequency bands alpha, theta, beta and delta. It can be fed back to a person in the form of auditory or tactile stimuli to monitor. A recent concern, however, is the use of neurofeedback for the purpose of training attention. Different devices and neurofeedback methods have been proposed to train EEG to use EEG activity to inform the user of the level of attention and to improve attention. U.S. Patent No. 5,377,100 relates to a method for improving people's attention, which provides a training method for enhancing the user's attention level by relating the user's attention level to the difficulty level video game through EEG detection hardware. do. Another US Pat. No. 5,447,166 proposes a neurocognitive adaptive computer interface system that monitors a user's EEG signal and physical response and uses a computer program to change the signal and response. Computer neural networks calculate a combination of EEG signals and other physiological variables that specifically characterize the trainee's level of attention. In addition, US Pat. No. 6,097,981 proposes a device that can analyze EEG response and control animation using a wireless infrared EEG biofeedback system. EEG signals can be used to control computer animation, one of which involves attention-enhancing games.

Recently, one of the most common psychotic disorders in children is Attention Deficit Disorder (ADD) and Attention Deficit & Hyperactive Disorder (ADHD) (hereinafter referred to as ADD / ADHD). In addition, quantitative EEG and neurofeedback therapy, which is applied to school disorders, parenting difficulties, and interpersonal problems, is an important treatment that improves children's attention and cognitive function and reduces behavioral problems. It is considered one of the U. S. Patent No. 6,097, 980 proposes a method of simplifying the number of frequency bands and the number of scalp attachment sites for QEEG diagnosis for ADHD. In other words, this method analyzes the FFT spectral power of two frequency bands (4.0-8.0 Hz and 13.0-21.0 Hz) at one brain location (Cz) with reference to the ear (AA). Therefore, the relationship between the ratio of theta to the power of theta at 4.0-8.0 Hz to the power of the slow beta recorded at 13.0-21.0 Hz was examined. The mean calculated in this way is compared with a normative database obtained from normal children without ADHD, to determine the presence and extent of ADHD. Neurofeedback for ADHD has been suggested as an effective alternative for both children and adult patients who do not see the effects of medication or have side effects, and studies on the effects of neurofeedback on ADHD treatment have been conducted through training. Reducing the theta / beta ratio and the amplitude of the precession bands has been shown to result in significant improvement.

The wireless RF transmission range represents an important advance in the EEG system, which is limited to several applications in the prior art that may be indirectly related to neurofeedback methods. U. S. Patent No. 5,755, 230 describes a wireless system program using several channels for RF transmission of physiological activity, including EEG for monitoring purposes. U.S. Patent No. 6,001,065 also discloses single channel RF monitoring for measuring and analyzing physiological signals (e.g., EEG and electromyograph; EMG) for active or passive control of various electronic media such as videogames, movies, virtual reality, and computer animation. Suggest a device. The system uses an infrared channel to transmit EEG signals to a computer via RF wireless transmission to control external devices such as home appliances.

The main advantage of the wireless RF transmission method is that the transmission range is wider than that of the conventional wired and infrared transmission methods, and the EEG system can be constructed with a small power. The problem with biomedical applications is that EEG radio RF transmission signals can be interfered by other, more powerful first-order commercial frequency waves in the frequency bands that can be used without permission (eg, around 900 MHz in the United States). . However, the recent application of spread spectrum technology for RF transmission to EEG wireless RF transmission can sufficiently eliminate such interference effects. Previous real-time EEG monitoring or neurofeedback radio system implementation techniques did not consider the application of such spread spectrum telemetry.

Although the systems and methods described so far all focus on real-time EEG analysis for neurofeedback use, including training aimed at improving the user's attention, none of EEG's spread spectrum wireless RF transmission biofeedback It does not focus on the systems and methods of improving attention or relaxation based on the system. In addition, conventional biofeedback systems have many limitations. For example, US Pat. No. 6,097,981 discloses a wireless device and method thereof in accordance with an EEG-based biofeedback system, but the described system uses infrared transmission, and its application is limited to attention enhancement games. The main disadvantage of the infrared transmission system lies in the straightness of the path between the transmitter receivers and the limitation on the range of distance. In other words, when the distance between the transmitter and the receiver is some distance and there is an obstacle between them, the signal may not reach the receiver or distortion may occur.

The prior art also has inaccuracies in actually extracting pure EEG signals. In other words, conventional techniques that assume that EEG is extracted from the scalp actually use EEG signals incorporating muscle activity (eg, EMG), and lack the technique of filtering muscle activity from EEG. In particular, systems for extracting EEG by attaching a headband type EEG sensor device to the forehead (assuming frontal lobe) are generated by the movement of muscles in the forehead, namely EMG (hereinafter referred to as EMG), as well as eye movement. It is a very inaccurate EEG system because it assumes that the signal, that is, safety (hereinafter referred to as EOG), or the signal in which noises are mixed, is assumed as an EEG. Therefore, it is impossible to teach some important voluntary control over EEG activities without maintaining the motivation for users to gain control over these systems. The success of the biofeedback system clearly has the means (eg games) to motivate the user to continue training by creating and adjusting the difficulty of control and by compensating for accurate changes in the target EEG parameters. Should be

Accordingly, the present invention recognizes these problems, and has made important improvements and developments not seen in the prior art.

First, the present invention greatly increased the flexibility of a system that can extract brain waves at various brain locations. For example, an electrode-attached headset system, a sub-system of the present invention, allows for the proper combination of EEG extraction positions and allows both monopolar and bipolar configurations to be used. In other words, using the earlobe or the posterior part of the ear as the reference point, the Fpz position, which is the center of the forehead, can be applied to the frontal lobe F3 and F4 or the sensory motor region C3 and C4, respectively. In this case, the anode induction method can be applied with the Fpz position as the ground point. In particular, when the unipolar induction method is applied to the signal extracted from the upper center Cz position of the skull with the rear projection of the ear as the reference point and the Fpz position as the grounding point, the noise caused by the movement of muscles or eyes is not affected.

In addition, the present invention selects a narrow subband of the EEG rhythm for neurofeedback training and achieves the convenience of the combination.

Furthermore, the present invention allows the user to safely connect to the computer wirelessly by applying a low power wireless RF transmission method.

Furthermore, the present invention first applied spread spectrum transmission and reception schemes known to minimize noise and interference from other wireless RF transmission sources.

The present invention allows the use of both traditional neurofeedback protocols and game-based training procedures that are directed at improving attention and enhancing performance. EEG activity parameters for visual or auditory feedback control are adjusted and selected in relation to the user's EEG characteristics indicating relaxation and activity status, and the goals and thresholds of neurofeedback training can be appropriately selected and adjusted in comparison with the normative database. Can be.

1 is a view showing a usage environment of a wireless neurofeedback system according to an embodiment of the present invention.

Figure 2 is a block diagram showing the configuration for transmitting brain signals used in the neurofeedback system of the present invention.

Figure 3 is a headset used for detecting the brain signal of the present invention and the internal electrode wire connection arrangement diagram.

4 is a RF transmission process of the brain signal by the neurofeedback system.

5 is a process for RF reception and neurofeedback execution of the brain signal by the neurofeedback system.

6 is a step-by-step flowchart for data processing of a neurofeedback system.

7 is a flowchart illustrating step by step a neurofeedback execution procedure according to the present invention.

As noted above, the present invention relates to systems and methods for the extraction, preprocessing, wireless RF transmission, real-time analysis and audiovisual presentation of EEG activity from at least two scalp locations.

Specifically, the configuration of the present invention is as follows.

Apparatus for neurofeedback training using a brain signal detector for detecting a bio-electrical signal from at least one scalp area of a user, sensing means for detecting a signal of the brain signal detector, and the signal detected from the sensing means And a wireless system for transmitting and receiving bidirectionally between a brain signal data transmission unit and a neurofeedback execution unit (host computer) by a wireless RF transmission method.

Here, the brain signal detection unit is composed of a multi-electrode sensor array for detecting at least one bioelectric signal (EOG, EMG, EEG) from the user's forehead or front-frontal lobe, frontal lobe, sensory motor region. Here, the headset is composed of a ground and a reference electrode set positioned at the center of the forehead and both the earlobe or the rear mastoid of the ear. In addition, the electrode sensor array can be selectively applied to the unipolar or bipolar induction method for bioelectric signal processing.

Here, the wireless RF transmitter including a detection and processing stage of the signal detected from the user's scalp through the electrode sensor array for the amplification, filtering, A / D conversion of at least one EEG signal and one EOG or EMG signal Electrical circuits. In addition, the bidirectional wireless RF signal transceiver includes a spread spectrum transmission and reception means for modulation and demodulation of at least one RF signal. However, instead of spread spectrum transmission and reception means, a frequency conversion key (FSK), a phase conversion key (PSK), and an FM conversion means may be used. In addition, the two-way wireless RF signal transceiver includes an RS-232C serial input and output device. The headset may be configured to include the multi-electrode sensor array.

The neurofeedback execution unit (computer) includes a computer program for processing real-time analysis of bioelectrical signals, a training protocol for neurofeedback and audiovisual feedback. In addition, the training protocol for neurofeedback includes a computer program in which an EEG signal is stored and that signal is compared with a previously stored EEG signal or normative database from the same user, and the threshold value or ballast stored in the computer memory. It includes a computer program that allows you to compare changes with base level or normative values.

Here, the training protocol for neurofeedback includes a computer program adapted to change the threshold based on a comparison of the EEG signal with at least one previously stored bioelectrical signal or normative database. The neurofeedback training protocol also includes a computer program that allows for setting threshold values based on previously stored EEG signals or normative EEG databases. Neurofeedback training protocols go through a series of steps to enhance the user's attention, measure the electrical activity of the user's brain, and analyze the measured signals in real time to reveal the intended changes through the analyzed electrical activity. By providing at least one feedback output signal through a visual display or an audio signal as a corresponding reward, the user is encouraged to maintain one level of electrical activity for a defined time. In addition, the neurofeedback training protocol promotes the user's attention by computer games in a series of stages, and transmits the measured brain electrical activity to the computer in the form of a wireless RF signal.

EEG was extracted from the appropriate scalp locations using a headset equipped with several EEG electrodes, including the reference electrode and the parabolic electrode (impedance of each electrode was kept below 10 kΩ). The extracted signal is transmitted to a computer through a wireless RF transceiver channel. EEG is filtered from noise such as EOG (electroculargram) and EMG, and the noise-free EEG signal is analyzed by real-time FFT by a computer built program. The parameters of the analyzed EEG activity are used for neurofeedback purposes.

For example, (1) the power index of EEG activity in the 4.0-8.0 Hz band divided by the EEG activity power in the 12.0-15.0 Hz band, or (2) the EEG power in the 13.0-18.0 Hz band EEG in the 4.0-12.0 Hz band. EEG spectral power indices, such as divided by power, are presented to the user and the user is rewarded by varying the indices and directions defined by pre-selected training protocols or computer game procedures.

The neurofeedback procedure and game design are adjusted to keep the user motivated enough to achieve better results during the neurofeedback training session. In each trial, the specific task difficulty is maintained at a level within the range obtained from the user's previous psychophysiological data and within the individual's VARIABILITY. Thresholds and goal selection in each individual neurofeedback training run, adjusted to individual levels, is an approach used to avoid too difficult or easy training goals within optimal levels and to maintain appropriate training task difficulty.

In the present invention, a neurofeedback procedure or an EEG control computer game may be used as a method for improving attention. EEG is extracted from the left frontal lobe (F3) and the right frontal lobe (F4), the center of the sensory motor region (Cz), the left sensory motor region (C3), or the right sensory motor region (C4). Each time the exponent based on the ratio of the EEG band approaches the target direction, the EEG power of the slow beta frequency band (13.0-18.0 Hz) increases at each scalp position and the low alpha or theta Whenever the EEG power in the frequency band (4.0-12.0 Hz) is reduced, it compensates the user. The invention can also be applied to training with focus-relaxed attention (eg, shooting athletes who require high concentration). This condition increases the spectral power of the fast alpha band (10.0-12.0 Hz) in the left hemisphere, simultaneously decreases the spectral power of the beta band (18.0-22.0 Hz), and theta band (6.0-8.0 Hz) in both hemispheres. Is achieved by increasing the spectral power of. Another neurofeedback training procedure is to maintain constant attention, increasing the power of the entire beta band (18.0-42.0 Hz) to achieve the goal of allowing users to perform tasks while maintaining high levels of attention. And reduce the spectral power of theta (4.0-8.0 Hz) and alpha (8.0-12.0 Hz).

The feedback signal may be of any standard type, i.e. bar, graph, chart, animation, and audio signal. It doesn't matter how the user gets information about his correct behavior, it's just a matter of whether he can get this feedback information in real time. Accordingly, the present invention provides a neurofeedback device and its method to enhance performance by allowing a user to increase attention and to obtain a target EEG pattern, as well as to achieve a convenient and wide range of applications by achieving system wirelessization. To provide.

In order to achieve the above object, the present invention provides a brain signal detection unit for detecting a bioelectric signal from various scalp positions, the brain signal data wireless transmission unit for detecting and processing the detected signal and then modulating it into a wireless RF digital transmission signal, wireless It includes a brain signal data radio receiver for receiving a transmitted signal and a signal to a host computer, and a neurofeedback execution unit for executing a program composed of protocols and algorithms for neurofeedback training according to the state of the bioelectrical signal.

The present invention, as well as the preferred embodiment, may be more clearly understood through the following detailed description of the invention and the description of the embodiments with reference to the accompanying drawings.

1 is a schematic diagram schematically showing a wireless neurofeedback system environment according to an embodiment of the present invention. According to FIG. 1, a headset 2 having multiple electrodes attached thereto is placed on the user's scalp 1 so that the active electrodes of the headset 2 can be used to measure microscopic units of tens of microvolts from a standard brain position (international 10/20 system). When the brain signal is detected, the detected signal is amplified in millivolts by the brain signal data wireless transmitter 3, subjected to analog filtering, converted to digital, and then modulated into an RF transmission signal and transmitted through the transmission antenna 4. The brain signal data receiver 6 is transmitted. The signal received from the receiving antenna 5 of the brain signal data receiving section 6 is demodulated again, encoded so that it can be read by the neurofeedback executing section 7, and enters the neurofeedback executing section 7 through the serial port. The neurofeedback execution unit 7 performs various signal analysis including a FFT on a signal in real time, and executes a neurofeedback protocol. Feedback to the neurofeedback is provided on the display 8 in visual form or in audio form through the audio speaker 9.

2, the neurofeedback wireless transmission and reception system according to the present invention is composed of a brain signal detection unit 1, a brain signal data radio transmitter 2, a brain signal data radio receiver 3 and a neurofeedback execution unit 4 have. The brain signal data radio transmitter 2 and the brain signal data radio receiver 3 / neurofeedback execution unit 4 are configured as bidirectional interfaces.

The brain signal detecting unit 1 detects three channels of EEG signals from the standard position of the user's scalp using electrodes. The three-channel electrodes detect brain signals that can be selectively applied by unipolar and bipolar induction from the forehead or pre-frontal, pre-frontal, and sensory-motor regions. The brain signal data wireless transmitter 2 is connected to the electrode wires 6 of the headset via the connector 7 and receives the signals detected from the electrodes. The signal entering the brain signal data radio transmitter 2 is amplified, preamplified and filtered through the analog signal conditioner 8. The adjusted signal enters the analog-to-digital converter 9 and is converted into a digital signal, which is modulated by the RF transceiver 13 via the low power microcontroller 10 and then wirelessly transmitted via the transmission antenna 14. RF transmission. The control of the brain signal data radio transmitter 2 is performed through the switch 12. The on / off switch 12A activates or deactivates the radio transmitter 2. The selector switch 12B is for selecting whether to display successive signal prompts and the results on the LCD 11. The operation switch 12C is for activating an operation mode that causes the radio transmitter 2 to start acquiring, processing, and transmitting a signal.

It is a preferred aspect of the present invention that EEG can be recorded by both monopolar and bipolar induction. That is, the bipolar induction method compares the signals of the right frontal lobe F4 and the corresponding left frontal lobe F3 based on Cz, which is the center of the cranial sensory motor cortex. Fpz, the front forehead portion, becomes the position of the ground electrode. Anodic induction involves attaching two active electrodes (eg, F3 and F4) to the ground electrode. At this time, the potential difference generated in each cortical region is measured. In some neurofeedback approaches, this bipolar induction around the centerline is preferred because it is relatively less affected by EMG noise. Another EEG recording technique is to use a standard reference position for unipolar induction. Monopolar induction involves attaching an active electrode at a location of interest for measurement. In this method, another electrode, called the reference electrode, is attached at a location with relatively little electrical activity. Examples of such locations include both ears A1, A2, earlobe AA, ear posterior mastoid, and the like. In the present invention, the body ear rear protrusion is used as the position of the reference electrode. Therefore, C3-A1 (left ear posterior protrusion), C4-A2 (right ear posterior protrusion), or Cz-ear posterior protrusion, respectively, is measured with Fpz in the ground position for unipolar induction.

The wireless RF signal transmitted from the transmission antenna 14 mounted on the brain signal data radio transmitter 2 is received by the reception antenna 15 mounted on the brain signal data radio receiver 3. The received signal is demodulated via the RF transceiver 16 and encoded in a serial manner via the microcontroller 17. The signal data enters the neurofeedback execution section 4 through the serial port 18. The neurofeedback execution unit 4 receives and analyzes the encoded signal, and also includes a host computer 19 that includes a neurofeedback protocol, a display 20 for providing feedback information via audiovisual modality to the user, and at least one of them. Peripherals such as a plurality of ideal audio-speakers 21 and a keyboard 22 for interfacing with the computer 19.

3 shows a headset 2 having a built-in multiple electrode used in a brain signal detecting device, and also shows an internal electrode wire connection arrangement of the headset. The headset 2 has a forehead band 56 positioned at the head circumference across the center of the forehead, a transverse band 57 positioned at the top of both ears across the top of the head, and longitudinally along the skull centerline at the center of the forehead. An endband 58 positioned to the middle, and two reference bands 59 and 59A connected by joints 54 at both ear positions of the forehead band 56 so as to be located at both ears or posterior mastoid of the ear. . The headset is made of elastic material so that proper pressure can be maintained and is designed to be adjusted using straps 61, 61A, 62, 62A to fit the size of the human head. Compatible, the headset also features built-in earphones, vibration sensors (such as bone conduction vibration) and microphones in the reference band position, making it a versatile neurofeedback and communication headset.

In the forehead band 56, an Fp1 electrode on the left side and an Fp2 electrode on the right side are disposed around the Fpz (ground electrode) at the center of the forehead. Indeed, forehead band electrodes detect EOG and EMG. In the transverse band 57, the electrodes of C3 on the left side and C4 on the right side of the center of Cz in the center of the skull are arranged to detect EEG from the sensory motor region of the center of the skull, and F3 and F4 at the front positions of C3 and C4, respectively. Electrodes are arranged to detect EEG from the frontal lobe region. C3-C4 and F3-F4 are selected according to neurofeedback protocol. One reference electrode A1 and one A2 are disposed in the two reference bands 59 and 59A, respectively.

Electrodes for detecting EEG are used in metals of gold-gold plated or silver-silver plated dry and wet type, and it is preferable to have a cup-shaped adhesive surface if a tooth is provided with an appropriate height in consideration of hair or an adhesive. . Special conductive adhesives (eg TEN20TM, Weaver & Co.) or EEG electrode creams (eg EC2TM, Grass Instruments) can also be used to keep the electrode low. All electrodes are connected to the connection 55 by a shield line inside the headset so that the brain signals detected by the electrodes enter the brain signal data transmitter.

4 is a unipolar induction method and forehead front surface measuring signals of C3 (23) and C4 (24) located in the sensory cortical region with respect to the ear or the rear protrusions A1 (22) and A2 (25) of the present invention. The typical implementation of the transmission terminal is configured to enable an anodic induction method for measuring the potential difference between Fp1 26 and Fp2 28 located in the prefrontal cortex region relative to position Fpz 27. The brain signal detected from the scalp is received by the transmitter through three channels. Wires from the electrodes attached to the headset are connected to the brain signal data radio transmitter for signal coordination and transmission via the connector 29. The transmitter is a small handheld device with the ability to continuously acquire, process and transmit brain signals. Power is generated by the built-in rechargeable battery 40 and the voltage regulator 41. The battery 40 is preferably a rechargeable 8V DC similar to that used in ordinary cell phones. For the safety of the user, the battery 40 can only be charged when the transmitter is disconnected from the headset.

The electrode wires are connected to the amplifier 30 or the preamplifier 30A differently for each channel. The preamplifier 30A amplifies an EEG having an analog bandwidth of at least 0.5-50.0 Hz, and is designed to have low external noise and a high amplification ratio. The preamplifier 30A of the transmitter provides an initial amplification ratio of the electrode signal 100. The amplified analog signals coming into channels 1, 2, and 3 pass through a band-pass filter 31, 32 and pass a frequency band of about 0.5 to 45.0 Hz before being converted to digital. Again pass through a low-pass or anti-alias filter (33, 34). Signals (EOG, EMG) from channel 1 are amplified directly through amplifier 30 without the operation of the preamplifier, and then digitally generated by the analog-to-digital converter 36 via the band pass filter 31 and the low pass filter 33. Although converted into a signal, the signal from channels 2 and 3 is first amplified by the preamplifier 30A, and then passes through the amplifier 35 through the band filter 32 and the low pass filter 34 and then the second order. Perform amplification. It is then converted into a digital signal through an analog-to-digital converter 36. The digitally converted signal is processed by a low power microcontroller 37 which is a digital processor. The analog-to-digital converter 36 provides digital processing with 256 sampling and 8 bits (or 12 bits) resolution per second.

When sampling the bipolar Fp1 (26) -Fp2 (28) brain signals from the frontal-frontal region through channel 1, three electrodes are needed. Two of these electrodes are used to measure the potential difference between Fp1 (26) and Fp2 (28). The potential difference input signal passes through the amplifier 30, and the other electrode is a reference electrode at the center of the forehead Fpz (27). Is located. Channel 1 transmits a signal having a larger amplification rate through the amplifier 30 through the band pass filter 31 and the low pass filter 33, and channel 2 and channel 3 are each obtained through a different preamplifier 30A. Signals having amplification rates are transmitted through different band filters 32 and low pass filters 34, respectively. Therefore, the signal of channel 1 mainly contains more EOG and EMG elements, which are potentials generated when the eye moves horizontally, which are included in the signals of channels 2 and 3 in the microcontroller 37. Used as a reference for filtering EOG noise.

The EEG signal detected by the C3 active electrode 23 located in the sensory motor region on the left side of the center of the skull and the A1 reference electrode 22 attached on the left ear or the rear projection is transmitted through channel 2, and the C4 position on the right side of the center of the skull. The EEG signal detected by the active electrode 24 attached thereto and the reference electrode 25 attached to the left ear rear protrusion A2 position is transmitted through channel 3. At this time, Fpz 27 is used as the ground electrode for channel 2 and channel 3 in this unipolar induction method.

Before the acquisition of the brain signal begins, the impedance of the electrode in the subject's scalp is measured by an impedance test circuit 38 controlled by the microcontroller 37, so that signals with known amplitude and source impedance are connected through the connectors. Via (29). The synthesized signal is measured by the microcontroller 37 through the process described above, and based on it, it is calculated whether the impedance of each electrode is suitable for the subject's scalp. If the impedance of any electrode attached to the subject's head is high, a warning message " HIGH " and an " OK " message are shown on the LCD display 15 if the electrode impedance is 10 kΩ or less. Therefore, when the electrode impedance test program is activated, the amplification factor is reduced by switching the active electrode input signal to a fixed square wave signal source for each channel. On the other hand, the output signal of each channel is also evaluated by the microcontroller 37 software so that the impedance of the individual electrodes is displayed more accurately in the 100 kΩ scale range.

Through the calibration circuit 39 for the internal calibration procedure, the active electrode inputs of all channels are switched to a fixed square wave signal source having a low impedance value. This simulates an electrode signal with a fixed amplitude that can be measured in the analog-to-digital converter 36 for calibration purposes. The calibration data is used to accurately estimate the EEG amplitudes recorded from channel 2 and channel 3.

The device subsequently acquires an EEG signal for analysis from the subject's scalp. The microcontroller 37 analyzes the calibrated values, encodes the result for RF modulation, and the encoded signal is modulated via the RF spectrum transceiver 42 and then transmitted. The spread spectrum conversion technique applied for the precise wireless EEG transmission method required by the present invention is a technique for "spreading" the RF transmission signal in a frequency band larger than the minimum bandwidth required for transmitting information. In this conversion technique, two data transmission methods commonly called frequency hopping and direct sequence are commonly used. The apparatus adopts frequency hopping. In addition, if a precise EEG transmission method is not required, a frequency shift key method, a phase shift key method, or an FM conversion method may be applied instead of the spread spectrum conversion method. This conversion method has the disadvantage of not being able to handle the data conversion precisely compared to the spread spectrum conversion method, but since it is much easier to construct, it is very simple in a simple application situation that does not require precise data such as medical diagnosis. This is the preferred way. The data transmission frequency range to meet spread spectrum radio transmission is the 902-928 MHz band, which can be used without permission in the industrial, scientific and medical fields.

The RF transmission of the present invention is implemented in both directions. That is, the transmitter can also receive the RF signal via the spread spectrum receiver 44 in the spread spectrum transceiver 42 via the antenna 4. The incoming signal is used to control the operation of the microcontroller 37, such as triggering the electrode impedance test, changing the signal acquisition channel, and the like.

FIG. 5 is a block diagram illustrating a process in which the brain signal data radio receiver 45 processes a radio signal transmitted from the brain signal data radio transmitter. The serial encoder 50 sends a serially encoded signal to the computer 7 through the RS-232C serial port 53 connected to the serial cable 52. The receiver 45 is also supplied with power via the serial cable 52, which is maintained at a constant voltage by the voltage regulator 51. The receiver 45 receives the RF signal transmitted from the transmission antenna through the reception antenna 5. The received signal is bidirectionally communicated via a spread spectrum transceiver 46 including a spread spectrum receiver 47 and a spread spectrum transmitter 48. Therefore, the RF signal may be transmitted to the transmission antenna through the reception antenna 5, and the transmitted signal may be used to control the operation of the microcontroller of the transmitter after demodulation. The receiver 45 uses spread spectrum modulation-demodulation format.

The received RF signal is decoded through a spread spectrum transceiver, and the decoded signal is encoded 50 for serial transmission via a microcontroller 49 and connected via a serial cable 52 connected to a serial port 53 to a computer ( 7) is sent to read.

FIG. 6 is a flowchart for explaining the flow of EEG input data, and is a view for showing a flow in which an EEG signal of one channel is processed step by step. Operation begins 101, and if a signal is detected 102 proceeds to the impedance check step 103. If the impedance is determined to be 10 kΩ or less, the EEG signal is advanced to the adjusting stage 201, and if it is higher, the signal returns to the signal detection step 102 again. The signal entering the EEG signal adjusting stage 201 is subjected to a preamplification step 104, a filtering step 105 by an analog filter having a fixed parameter, and the filtered signal is again subjected to an amplification step 106. Through the digital conversion step 107 is converted into a digital signal. The digitally converted signal enters the signal transmission and reception stage 202 via a step 108 of removing EOG or EMG noise through an additional adaptive filter.

The noise-free pure EEG digital signal entering the signal transmission and reception stage 202 is modulated (109) into an RF signal and transmitted (110). The transmitted modulated signal is checked at step 111 of the transmitting and receiving end 202, if it is determined to be an RF signal, proceed to the next step 112, otherwise returning to step 111. The signal determined to be an RF signal is demodulated (112) and noise is removed via the RF noise filtering step 113. The noise-free RF signal is amplified again (114) and serially encoded and transmitted to the digital signal processing stage 203 through the serial port (115).

The signal entering the digital signal processing stage 203 passes through an analysis step 116 such as a real-time FFT to calculate the power and ratio parameters of the selected EEG band (117). In a next step, the computer program compares and classifies the EEG pattern with a preset threshold (118).

This analysis value actuates the feedback stage 204 to implement graphics or charts as a display for visual feedback (119) or to generate audio feedback (120). It is then determined in step 121 whether or not to continue the trial, which continues to return to the digital processing unit 203, otherwise terminates (122).

To set goals and thresholds for neurofeedback training, we first define changes in selected parameters, such as the power of the slow beta, compared to at least one previous EEG record or norm database for users with the same ballast baseline prior to the training session. Used as a basis for If the goal of the training session is to increase slow beta power, the threshold is set at a level above 20% of the baseline. Thus, if the increase in parameters is more than 20% above the baseline level, it is compensated by visual or auditory feedback signals and the score of the trial is presented on the display. The score may be presented in terms of the total number of times the threshold has been exceeded or the number of hits, that is, the number of times the target parameter has crossed the threshold level for a predetermined time. In the behavioral protocols, the thresholds in each trial are adjusted for continued levels of EEG activity, and tasks become increasingly difficult to achieve better results. If the subject does not change the target parameter to the required degree, the task difficulty becomes easier. In the end, reward criteria can be set through increasing or decreasing difficulty as needed to facilitate learning.

Six possible embodiments of the general EEG neurofeedback procedure according to the present invention are illustrated below.

1. When the slow beta (13.0-18.0 Hz) above the level of the pre-stabilizer occurs above or above the skull, the user can be compensated statically by compensating with visual or auditory feedback signals.

2. If theta (4.0–8.0 Hz) exceeds the prestabilizer plateau level, provide additional compensation feedback signals when reducing theta below the baseline level.

3. Compensation can be made for both 1 and 2.

4. Acoustic or visual feedback compensation when fast beta (20.0-30.0 Hz) approaches the pre-train plateau level.

5. Compensation for 3 and 4 above.

6. In addition, new combinations may be formed according to consolidation plans.

The general neurofeedback training idea according to the present invention has the same objective implementation of changing the target EEG pattern in the intended direction through compensation regardless of which combination described above is used. Moreover, some combinations may produce better results for some users while others may produce better results. Thus, other parameters can be selected as neurofeedback training targets. The standard approach to using neurofeedback involves the following steps:

1. SMR increases and theta decreases.

2. Slow beta increases in the center of the skull and theta continues to decrease.

3. SMR increases and slow beta increases, but theta continues to decrease and maintain.

4. When slow beta and theta stabilize and fast beta increases, theta reduction feedback compensation stops.

Auditory feedback compensation sounds are the main description for real-time feedback used for EEG training in accordance with the present invention. The notes should be presented with sufficient volume and duration for rapid self-control learning. Sound quality and acoustic characteristics are also important for controlled learning. For example, negative presentation can interfere with EEG alpha generation. Therefore, for the purpose of alpha training according to the present invention, a sound between about 400 Hz and 800 Hz is selected as a feedback sound that minimizes the interference of alpha generation. However, in theta training, the main problem is the subject's drowsiness that can fall to sleep. Therefore, for theta training purposes, the feedback sound may be a high pitch sound of 800 Hz or more or a low pitch sound of about 400 Hz or less (preferably 200 Hz). These feedback sounds may be digital synthesized sound, digital recording, music, or the like. Therefore, you can set the default frequency according to your options so that the appropriate frequency sounds can be generated for the purpose of training: theta training (800 Hz), alpha training (400 Hz), SMR training (1300 Hz), slow beta training (1500 Hz). ).

Another way to feed back EEG parameters is by using a visual display. The visual display is not limited to any particular form. The most traditional forms used in biofeedback are linear and bar graphs, pie charts, tables, spectral displays, and EEG waveforms. Either way, the conditions to be met are the availability of real-time monitoring of the threshold and the EEG parameters selected for feedback (eg, slow alpha band power). Typically, several screens are split for biofeedback display so that the most preferred one is selected prior to training. Auditory and visual feedback can be used independently or simultaneously, depending on the training protocol selected.

7 is a flowchart for explaining step-by-step neurofeedback training procedure according to the present invention. The user proceeds a training step 300 to promote self-control over EEG parameters through feedback by both auditory and visual stimuli. Prior to the commencement of training, the user sits comfortably in an armchair seated in a frontal position where the display of the neurofeedback training system can be observed and rests for a short time (approximately 5-10 minutes). Listen to the explanation. At this time, the distance between the monitor and the user should be maintained at a sufficient distance (about 2m or so) so as not to interfere with visual and auditory feedback. Once the procedure begins (301), the user secures the headset with the electrode attached to the head (302). As shown in FIG. 3, the headset is arranged with one ground electrode, two reference electrodes, and active electrodes positioned in various cortical regions. The next step 303 checks if the user is an existing user. If the user is a new user, register for the program, enter the user's information (eg, age, last name, address, etc.) for the user information database and proceed to the next step 304. Step 304 performs a pre-psychological test (eg, mood scale questionnaire, etc.) on the user.

Acquisition of EEG data by step 305 proceeds according to FIG. 6, which is generally preceded by a plateau level test. First, the baseline level is recorded 306 in the "opened eye" state to obtain a typical EEG activity profile of the user, through which normalization and adjustment of all operational states of the system is made. Base-level recording in "opened eyes" is typically made while a small bright light (e.g. LED) and continuous background sound are present, allowing the user to focus on any point or object on the display. The baseline level in the "eyes closed" is then recorded (307), to determine the nature of EEG activity in the absence of general sensory stimulation. This step is performed while only a continuous background sound is presented. In a next step 308 the user is exposed to a series of stress-induced stimulus situations. For example, loud explosive sounds (greater than about 80 dB), computational tasks, reading difficult reading materials, puzzles or other available cognitive tasks are used. The stress or arousal state caused by this step 308 proceeds to a more complex psychological task, ie, attention or emotional image task, in the next step 309. Response profiles can be assessed more accurately.

Thereafter, the relaxation step 309 is performed, in which previously obtained psychophysiological data are used to evaluate the EEG pattern of the user obtained during the self-induced relaxation step 309, and then the training result is provided to the user. It is a material to inform. EEG response and relaxation profiles are entered into the user's personal database. If an appropriate normative database is established, the baseline and stress EEG files are compared with the normative data (310) and proceed to the next step. After comparing the normative database with the user's EEG pattern database, a neurofeedback session type to be concentrated in the next step 311 is defined. Then set goals and thresholds for training (312). In the next step 313, the detailed training procedure and the goal of the training are explained and directed to the user. If the user has a question or question, he or she will explain it, but do not provide any hints about how to increase neurofeedback control. The principle of neurofeedback success is to allow users to discover for themselves the most effective strategy to achieve target EEG parameter control, while visual and auditory feedback rewards compensate for desirable changes, but not for inaccurate changes. To help users learn the most effective change strategies.

Once all training parameters have been set in step 314 and the check to complete the actual training is complete, determine the number of trials to proceed with the actual training. The set trial then proceeds (315), which takes about 120 seconds. Standard training runs include providing visual and auditory feedback as EEG parameters change within a preselected frequency band. Once a trial has been completed, the user will rest for 8-10 seconds before deciding whether to try the next trial. During the break, auditory feedback is stopped and the performance score for the completed trial, ie each activity EEG parameter or Results are provided for the selected EEG parameter combination (316).

Thereafter, in step 317, it is determined whether to repeat the training. The continuation of the trial returns to step 315 and is repeated by the number of trials defined in step 314. If the decision is not to repeat the trial anymore, the baseline level record (318) and the baseline level record (319) of the "eyes closed" after the session to determine the overall outcome of the selected EEG training session. Proceed in turn. Step 320 then presents a record summary of the training session. Then, after the session, the mood scale questionnaire or another psychological questionnaire is performed (321). In the next step 322, the results of the EEG and psychometric data changes obtained through the training session are compared with the user's psychological and EEG activity database or normative database to present a comprehensive judgment result. If in step 323 a new training session is to proceed, the process returns to step 311, where a new training session is set up based on the modified training targets and thresholds based on the results of previous training runs. If no new training session is desired, training ends (324).

According to the neurofeedback wireless system and method according to the present invention, several training goals can be pursued through training sessions. One of them is to acquire EEG suppression (eg, theta wave power suppression in the 4.0-8.0 Hz band) by improving the control of the user to consciously suppress EEG activity. Another goal is to increase EEG (e.g., increase the SMR in the 12.0-15.0 Hz band), in which the user improves control by trying to increase EEG activity in response to feedback. After the user completes the training session, the indicator analyzes the data and interviews the user about what EEG control needs to be strengthened. Interviews can dictate the effectiveness of the strategy by letting the user dictate the strategy used and are important to reinforce the strategy for future neurofeedback training. However, the present invention can be used without an indicator. By building a program that provides sufficient instruction to the system itself, the user can use a self-implemented neurofeedback training system without an indicator. In other words, by establishing EEG training supervision in the system, training sessions can be conducted according to programmed instructions.

An example application of the present invention is the use of protocols for the treatment of ADD / ADHD. Typical neurofeedback training protocols for ADD / ADHD treatment inspire the 12.0-15.0 Hz band (SMR) or the 15.0-18.0 Hz band (slow beta) while simultaneously providing the 4.0-8.0 Hz band (theta) and the 22.0-30.0 Hz band (fast Beta). Active electrodes are also typically located in the sensorimotor cortex (Cz, C3, and C4 according to the International 10-20 System) and reference electrodes are located in the earlobe to measure EEG by unipolar induction.

The neurofeedback training protocol according to the present invention compensates for increasing the EEG amplitude of the 13.0-18.0 Hz band (slow beta of 15.0-18.0 Hz and SMR of 12.0-15.0 Hz) in the sensorimotor cortex (C3 or C4). At the same time, it suppresses excessive EEG activity in the low frequency band of 4.0-8.0 Hz and the high frequency band of 22.0-40.0 Hz. If left hemisphere SMR training (C3) is performed, only slow beta rewards will be provided, and for right hemisphere training only SMR rewards will be provided. This protocol can also be applied to the treatment of epilepsy, headache, blood pressure, cardiovascular and drug abuse (alcoholism, drugs, etc.).

In addition to the application of disability or disease problems, these training protocols can also be applied to improve performance for normal people, such as neurofeedback sports computer games (eg golf, shooting, billiards, etc.). For example, a golf putting training game determines the speed and direction of the golf ball rolling towards the cup at the putting surface based on the user's EEG activity parameters. With the internal concentration of your EEG pattern prior to your putt, you may have access to an EEG pattern database (typically databased EEG patterns when experienced golfers make good putts) that involves a good pre-built good putt run. Better putting results can be obtained to improve performance and control, while also providing entertainment.

Thus, another object of the present invention is to provide a game that allows training based on neurofeedback that allows the user to improve their ability to pay attention to putting performance (sports researchers call this peak performance). Along with this, it is also an object of the present invention to instruct the golfer performer to accurately inform the state of his EEG activity so as to continuously change the EEG parameters so that the intended focus and concentration can be reached prior to putting. The training protocol is to increase the EEG power of the low frequency band 6.0-12.0 Hz band in the left hemisphere while reducing the EEG power of the higher frequency band 18.0-42.0 Hz band.

In addition, the neurofeedback training protocol allows direct transmission and reception with directional and kinetic machinery (e.g. toys, robots, etc., etc.) instead of the host computer, which is a neurofeedback implementation that transmits and receives bidirectionally with the brain signal data transmitter. Can be implemented. In other words, a DSP chip can be built in the brain signal data transmitter to transmit a specific brain signal parameter analyzed in advance in the form of an RF signal to a mechanical device having direction and movement. Thus, real-time analysis of brain signals on a host computer can be performed on the DSP chip to achieve specific training goals while reducing the load on the data transfer. In this case, the FSK method is preferable for the radio transmission method of the signal.

The micro-controller is built into the mechanical device with the direction and movement, which adjusts the direction and the movement speed according to the transmitted brain signal parameters to induce the user to enjoy the neurofeedback training more boringly. You can do it. It will be a neurofeedback wireless system that is well suited for the general public as well as ADD / ADHD users.

According to the present invention, by converting the conventional EEG biofeedback wired system to a completely wireless system has the advantage that can increase the utility of this kind of system. That is, the existing wired system was inconvenient by constructing an interface through many wires between the user and the system, and was also greatly exposed to the influence of noise coming from the wires. It can eliminate noise coming into the wires. In addition, the few inventions that have realized the wireless biofeedback system by recognizing this point are at best semi-wireless or infrared due to technical difficulties, i.e., the loss of data due to large data loss due to general wireless RF transmission and reception. Although the present invention is adopted, the present invention can significantly overcome the limitations of the existing method by applying the spread spectrum conversion method, and thus, a full wireless neurofeedback system can be implemented, thereby greatly extending its application range.

The effect of the present invention is to greatly increase the distance between the system and the user, to minimize and reduce the size of the system, to control multiple users with one system, thereby realizing a user-centered interface, precise EEG spectrum Due to the division of, various training protocols can be applied, and EEG can be applied to various analysis methods such as fast Fourier, neural network, multi-channel EEG phase analysis, time-frequency analysis (TFA), and event-related potential analysis in real time. As a result, it can be used as a neurofeedback training tool for treating disorders and diseases, as well as improving performance for the general public and professionals, and it is possible to perform neurofeedback training and online games with interest and entertainment through computer games. It works.

Although the present invention has been disclosed for purposes of illustration and description, it is not intended to be limited to the form of the foregoing embodiment. Those skilled in the art will appreciate that many variations and modifications are possible. While the present invention has been selected and disclosed with respect to the preferred embodiments described above in order to illustrate the best principles of the invention, those skilled in the art will recognize that various modifications and changes can be made without departing from the spirit and scope of the invention. .

Claims (24)

A brain signal detector for detecting a bioelectrical signal from at least one scalp region of a user; Sensing means for sensing a signal of the brain signal detector; Wireless system for neurofeedback training using the brain signal parameters for transmitting and receiving the signal detected by the sensing means between the brain signal data transmission unit and the neurofeedback execution unit (host computer) in a wireless RF transmission method. The method of claim 1, wherein the brain signal detection unit Wireless system for neurofeedback training using brain signal parameters consisting of a multi-electrode sensor array for detecting at least one bioelectrical signal (EOG, EMG, EEG) from a user's forehead or front-frontal, frontal, or sensory motor areas. The method of claim 2, wherein the electrode sensor array, Wireless system for neurofeedback training using brain signal parameters with selective application of unipolar or bipolar induction for bioelectrical signal processing. The wireless RF transmitter of claim 2, wherein the wireless RF transmitter of the signal detected from the user's scalp through the electrode sensor array comprises an electrical circuit for amplifying, filtering, and A / D conversion of at least one EEG signal, one EOG or EMG signal. Wireless system for neurofeedback training using brain signal parameters, including. 5. The system of claim 4, wherein the impedance of each electrode is maintained below 10 kΩ when extracted from the appropriate scalp locations using a headset equipped with several EEG electrodes. The method of claim 1, wherein the two-way wireless RF signal transceiver Neuros using spread signal transmission / reception means for modulation and demodulation of at least one RF signal, spread spectrum transmission / reception means, and brain signal parameters including frequency conversion key (FSK), phase conversion key (PSK), and FM conversion means. Wireless system for feedback training. The method of claim 6, wherein the two-way wireless RF signal transceiver A wireless system for neurofeedback training using brain signal parameters including an interface between a wireless RF signal transceiver and a neurofeedback execution unit (computer) using an RS-232C serial input / output device. The method of claim 7, wherein the input signal of the brain signal from a computer, A computer analysis program for processing real-time analysis of bioelectric signals; Multiple choice training protocol for neurofeedback, Wireless system for neurofeedback training using brain signal parameters including visual and auditory feedback. 10. The neurofeedback of claim 8, wherein the multiple training protocol for neurofeedback comprises a computer program comprising a computer program that allows an EEG signal to be stored and compared with a previously stored EEG signal or normative database from the same user. Wireless system for training. 10. The system of claim 9, wherein the EEG signal comprises a computer program for comparing the EEG signal with a threshold or ballast base level or norm value stored in computer memory. The method of claim 10, wherein the training protocol for neurofeedback is Wireless for neurofeedback training using brain signal parameters, including a neural network analysis computer program that allows the threshold to be changed based on a comparison of the EEG signal with at least one previously stored bioelectrical signal or normative database. system. The method of claim 10, wherein the neurofeedback training protocol is A wireless system for neurofeedback training using brain signal parameters including a computer program that allows setting thresholds based on previously stored EEG signals or normative EEG databases. The method of claim 10, wherein the neurofeedback training protocol is Go through a series of steps to promote user relaxation and attention, Measure the electrical activity of the user's brain, Analyze the measured signal in real time, In the event that an intended change is detected by the analyzed electrical activity, the corresponding compensation is provided by providing at least one feedback output signal via visual display or audio signal, A wireless system for neurofeedback training using brain signal parameters characterized by inducing a user to maintain one level of electrical activity for a defined time. 10. The method of claim 9, wherein the neurofeedback training protocol is A wireless system for neurofeedback training using brain signal parameters characterized by enhancing the performance, relaxation, attention and concentration of the user by a computer game in a series of stages. 10. The method of claim 9, wherein the neurofeedback training protocol basically provides theta training, alpha training, SMR training, slow beta training according to the user's training target requirements. The application protocol features a selection of training protocols, which are combinations of basic training protocols: slow beta increase-theta decrease, SMR increase-theta decrease, alpha increase- theta increase-alpha / theta increase, slow alpha increase-theta increase-beta decrease Wireless system for neurofeedback using brain signal parameters. 10. The wireless system of claim 9, wherein the neurofeedback training protocol transmits the measured electrical activity of the brain to a computer in the form of a wireless RF signal. 10. The method of claim 9, wherein the neurofeedback training protocol transmits specific electrical activity of the measured brain, such as a toy car, a toy train or a robot, to a machine with mobility in the form of a wireless RF signal in a frequency conversion key (FSK) manner. Wireless system for neurofeedback training using the brain signal parameters, characterized in that. 18. The method of claim 17, wherein the neurofeedback training protocol transmits a specific brain signal parameter analyzed using a DSP chip to a machine having a direction and mobility in which a control device by a microcontroller is constructed in the form of an RF signal. Wireless system for neurofeedback training using EEG parameters. The method of claim 2, A headset comprising the multi-element electrode sensor array. The method of claim 16, The headset, A wireless system for neurofeedback training using EEG parameters consisting of ground and reference electrode sets positioned at the center of the forehead and at both ears or on the posterior mastoid of the ear. Detecting a brain signal from at least one scalp region of the user; A sensing step of sensing a signal of the brain signal detector; The wireless method for neurofeedback training using the EEG parameter for transmitting and receiving the signal detected by the sensing means between the brain signal data transmission unit and the neurofeedback execution unit by a wireless RF transmission method. The method of claim 21, wherein the brain signal detecting step A wireless method for neurofeedback training using EEG parameters consisting of a multi-electrode sensor array for detecting at least one bioelectrical signal (EOG, EMG, EEG) from a user's forehead or front-frontal, frontal, or sensory motor areas. The method of claim 21, wherein the electrode sensor array, Wireless method for neurofeedback training using EEG parameters with selective application of unipolar or bipolar induction for bioelectrical signal processing. 22. The training protocol for neurofeedback according to claim 21, wherein the training protocol for neurofeedback comprises neurofeedback training using an EEG signal comprising a computer program such that the EEG signal is stored and compared to a pre-stored EEG signal or normative database from the same user. Wireless way for you.