阅读说明：本技术 一种基于便携式eeg设备的脑部健康筛查方法 (Brain health screening method based on portable EEG equipment ) 是由 卢树强 王晓岸 郭建明 格温妮斯·温妮·吴 于 2019-08-02 设计创作，主要内容包括：本发明公开了一种基于便携式EEG设备的脑部健康筛查方法,包括脑电信号采集判别步骤,所述脑电信号采集判别步骤为：第一步：通过1-4导联的高灵敏度电极片进行信号感应,并进行原始脑电信号采集,原始信号通过阻抗测试模块进行信号稳定性测试；本发明通过脑电便携采集原始脑线信号后,进行同步信号放大,并通过无线传输进行脑电信号传输,并实现在移动端进行脑电信号接收,接收后的信号与云端通信进行存储,并通过脑电预处理算法,脑点信号识别算法,及对应的脑电机器学习算法和深度学习算法进行信号识别,通过对不同类型和状态下人们大脑信号对采集数据的分析和计算,来进行大脑功能,和大脑精神健康方面的多项指标的测量和评估。(The invention discloses a brain health screening method based on portable EEG equipment, which comprises an electroencephalogram signal acquisition and judgment step, wherein the electroencephalogram signal acquisition and judgment step comprises the following steps: the first step is as follows: carrying out signal induction through a 1-4-lead high-sensitivity electrode plate, collecting original electroencephalogram signals, and carrying out signal stability test on the original signals through an impedance test module; according to the invention, after an original brain wire signal is portably collected by brain electricity, synchronous signal amplification is carried out, brain electricity signal transmission is carried out through wireless transmission, brain electricity signal receiving is carried out at a mobile terminal, the received signal is stored in cloud communication, signal identification is carried out through a brain electricity preprocessing algorithm, a brain point signal identification algorithm, a corresponding brain electricity machine learning algorithm and a depth learning algorithm, and measurement and evaluation of multiple indexes in the aspects of brain functions and brain mental health are carried out through analysis and calculation of collected data of brain signals of people in different types and states.)

1. A brain health screening method based on a portable EEG device, characterized by: the method comprises an electroencephalogram signal acquisition and discrimination step, wherein the electroencephalogram signal acquisition and discrimination step comprises the following steps:

the first step is as follows: carrying out signal induction through a 1-4-lead high-sensitivity electrode plate, collecting original electroencephalogram signals, and carrying out signal stability test on the original signals through an impedance test module;

the second step is that: when the signals are stable, the original electroencephalogram signals are transmitted to an operational amplifier for signal amplification and signal intensity optimization;

a third part: the signals after being amplified and optimized are transmitted to a mobile terminal through a Bluetooth module, and the current physical state is judged through an acceleration sensor;

the fourth step: the mobile terminal transmits the received signals to a cloud high-performance computing server cluster for signal analysis and computation;

the fifth step: and the electroencephalogram signal mode recognition algorithm, the electroencephalogram machine learning algorithm and the electroencephalogram deep learning algorithm are used for carrying out calculation and judgment on related indexes such as brain health, brain function and the like on signals corresponding to electroencephalogram states and events.

2. The portable EEG apparatus based brain health screening method of claim 1, wherein: the signal acquisition and processing flow in the first step is as follows:

the first step is as follows: a user wears electroencephalogram monitoring equipment, and after a communication test is carried out, original electroencephalogram signals are transmitted and stored;

the second step is that: carrying out data preprocessing on the originally acquired signals, carrying out signal transformation through a Fourier algorithm, and converting the signals into digital signals;

the third step: noise removal and conversion change are carried out on the electroencephalogram signals converted into digital signals through a filtering algorithm and principal component analysis, and the removal is mainly carried out on the oculomotor interference, the electrocardio interference, the myoelectricity interference, the power frequency interference, the high-frequency noise interference and the like through a regression method, a self-adaptive filtering method and an independent component analysis method;

the fourth step: and uploading the original electroencephalogram signals subjected to noise removal to a storage server, and synchronously transmitting the original electroencephalogram signals to an electroencephalogram algorithm calculation server.

3. The portable EEG apparatus based brain health screening method of claim 1, wherein: the signal analysis and signal analysis calculation process in the fourth step is as follows:

the first step is as follows: extracting the characteristics of the original electroencephalogram signals, determining various parameters by taking the characteristic signals as source signals, and forming characteristic vector transformation representing the characteristics of the signals by taking the parameters as vectors;

the second step is that: performing time domain signal parameter extraction and transformation feature engineering on the original electroencephalogram signal vector features;

the third step: carrying out frequency domain signal parameter extraction and transformation characteristic engineering on the original electroencephalogram signal vector;

the fourth step: the signal characteristics and the electrode position relationship are used to classify the characteristics by using autoregressive, fourier transform, surface laplace transform, wavelet transform, and the like.

4. The portable EEG apparatus based brain health screening method of claim 1, wherein: and in the fifth step, identifying the mode of the electroencephalogram signal and judging a result:

the first step is as follows: in the initial stage, the electroencephalogram signals are analyzed in a time domain and a frequency domain, and feature extraction is carried out, from signal and noise identification of initial signal acquisition to filter algorithm setting, and through different parameter adaptations, the most effective signal-to-noise ratio signals acquired based on electroencephalogram EEG equipment are obtained;

the second step is that: the original effective electroencephalogram is obtained, and simultaneously, signal transformation, normalized frequency spectrum and power spectrum analysis, time sequence signal change fluctuation analysis, corresponding characteristic algorithm realization optimization adaptation are carried out, and measurement and judgment of indexes of brain health, brain function and mental state under the conditions of different events and related states are carried out.

5. The portable EEG apparatus based brain health screening method of claim 1, wherein: the pattern recognition and result judgment process of the brain electrical machine learning in the fifth step is as follows:

the first step is as follows: the extraction of the modeling characteristics of the EEG data processed by the signal analysis algorithm is realized, and meanwhile, algorithm models such as a machine learning model, a decision tree, naive Bayes classification, a least square method, logistic regression, an integration method, a support vector machine, a clustering algorithm principal component analysis, singular value decomposition, independent component analysis and the like are introduced;

the second step is that: through the machine learning model selected in the first step and through the labeled training of the model, index classification and automatic discrimination under different brain health and cognitive function events and states are respectively carried out on EEG signal characteristics.

6. The portable EEG apparatus based brain health screening method of claim 1, wherein: and the fifth step is a pattern recognition and result judgment process of brain electrical deep learning:

the first step is as follows: for large-scale user monitoring and identification, along with the robustness requirement and the automatic updating requirement of an electroencephalogram signal identification algorithm, along with the increase of the user size and the increase of the data set scale, the traditional signal analysis and machine learning algorithm cannot meet the requirements on algorithm updating and automatic identification efficiency, and the design of an EEG electroencephalogram signal processing model of an artificial intelligence algorithm is developed;

the second step is that: end-to-end deep learning modeling and operation are carried out on a GPU high-performance server by introducing a deep learning neural network, training is carried out mainly on the basis of partial labeled EEG (electroencephalogram) data based on a cyclic neural network structure and a convolution upgrading network structure to form a classifier and a discriminator, and the classification is carried out on the EEG data

The third step: and meanwhile, data sets are continuously expanded, the neural network model algorithm is continuously updated, and the identification precision and accuracy of indexes of brain health, brain cognition and neural state are continuously improved.

Technical Field

The invention belongs to the technical field of EEG (electroencephalogram) signal identification, and particularly relates to a brain health screening method based on portable EEG equipment.

Background

Brain cognition is the process of acquiring knowledge or applying knowledge by converting external information received by human brain into internal psychological activity through processing treatment. Cognitive disorders refer to impairment of one or more of the above cognitive functions and affect the daily or social abilities of an individual. Brain health is the intact and physiological brain. Biochemical metabolism is in a relative equilibrium state; in terms of reflecting function or in terms of cognitive psychology, the health of the brain is that the external stimulus and the reflecting process and result of the brain have relative consistency and maintain dynamic balance; in terms of individual experience or social meaning of individual experience, brain health is a dynamic balance between a rather stable system of experience of the brain and a constantly changing social reality. The main measurement of cognitive function, brain health and mental health is observed and recorded by questionnaires and psychological scales and some behavioral tests, and distinguished according to the characteristics of the corresponding psychologist or clinician in combination with electroencephalogram.

The method has the problems that for most groups, questionnaires are filled randomly, information is distorted frequently, and result judgment is wrong greatly. The interviews of psychologists are inconsistent in level, and the obtained conclusions cannot be unified. The electroencephalogram equipment is generally used for diagnosing diseases with obvious other physical characteristic diseases in hospitals, the equipment is relatively rare and difficult to be allocated to patients with chronic mental and brain and cognitive functions for detection, and therefore a brain health screening method based on portable EEG equipment is provided.

Disclosure of Invention

The invention aims to provide a brain health screening method based on portable EEG equipment, which aims to solve the problems that the filling of questionnaires is random and the information is often distorted, so that the result judgment has larger errors for most groups. The interviews of psychologists are inconsistent in level, and the obtained conclusions cannot be unified. The electroencephalogram equipment is usually used for diagnosing diseases with obvious other physical characteristic diseases in hospitals, and the equipment is relatively rare and difficult to be allocated to patients with chronic mental and brain and cognitive functions for detection.

In order to achieve the purpose, the invention provides the following technical scheme: a brain health screening method based on portable EEG equipment comprises an EEG signal acquisition and discrimination step, wherein the EEG signal acquisition and discrimination step comprises the following steps:

the first step is as follows: carrying out signal induction through a 1-4-lead high-sensitivity electrode plate, collecting original electroencephalogram signals, and carrying out signal stability test on the original signals through an impedance test module;

the second step is that: when the signals are stable, the original electroencephalogram signals are transmitted to an operational amplifier for signal amplification and signal intensity optimization;

a third part: the signals after being amplified and optimized are transmitted to a mobile terminal through a Bluetooth module, and the current physical state is judged through an acceleration sensor;

the fourth step: the mobile terminal transmits the received signals to a cloud high-performance computing server cluster for signal analysis and computation;

the fifth step: and the electroencephalogram signal mode recognition algorithm, the electroencephalogram machine learning algorithm and the electroencephalogram deep learning algorithm are used for carrying out calculation and judgment on related indexes such as brain health, brain function and the like on signals corresponding to electroencephalogram states and events.

Preferably, the signal acquisition and processing flow in the first step is as follows:

the first step is as follows: a user wears electroencephalogram monitoring equipment, and after a communication test is carried out, original electroencephalogram signals are transmitted and stored;

the second step is that: carrying out data preprocessing on the originally acquired signals, carrying out signal transformation through a Fourier algorithm, and converting the signals into digital signals;

the third step: noise removal and conversion change are carried out on the electroencephalogram signals converted into digital signals through a filtering algorithm and principal component analysis, and the removal is mainly carried out on the oculomotor interference, the electrocardio interference, the myoelectricity interference, the power frequency interference, the high-frequency noise interference and the like through a regression method, a self-adaptive filtering method and an independent component analysis method;

the fourth step: and uploading the original electroencephalogram signals subjected to noise removal to a storage server, and synchronously transmitting the original electroencephalogram signals to an electroencephalogram algorithm calculation server.

Preferably, the signal analysis and signal analysis calculation process in the fourth step is as follows:

the first step is as follows: extracting the characteristics of the original electroencephalogram signals, determining various parameters by taking the characteristic signals as source signals, and forming characteristic vector transformation representing the characteristics of the signals by taking the parameters as vectors;

the second step is that: performing time domain signal parameter extraction and transformation feature engineering on the original electroencephalogram signal vector features;

the third step: carrying out frequency domain signal parameter extraction and transformation characteristic engineering on the original electroencephalogram signal vector;

the fourth step: the signal characteristics and the electrode position relationship are used to classify the characteristics by using autoregressive, fourier transform, surface laplace transform, wavelet transform, and the like.

Preferably, in the fifth step, the electroencephalogram signal pattern recognition and result judgment process:

the first step is as follows: in the initial stage, the electroencephalogram signals are analyzed in a time domain and a frequency domain, and feature extraction is carried out, from signal and noise identification of initial signal acquisition to filter algorithm setting, and through different parameter adaptations, the most effective signal-to-noise ratio signals acquired based on electroencephalogram EEG equipment are obtained;

the second step is that: the original effective electroencephalogram is obtained, and simultaneously, signal transformation, normalized frequency spectrum and power spectrum analysis, time sequence signal change fluctuation analysis, corresponding characteristic algorithm realization optimization adaptation are carried out, and measurement and judgment of indexes of brain health, brain function and mental state under the conditions of different events and related states are carried out.

Preferably, the pattern recognition and result judgment process of the learning of the brain motor in the fifth step is as follows:

the first step is as follows: the extraction of the modeling characteristics of the EEG data processed by the signal analysis algorithm is realized, and meanwhile, algorithm models such as a machine learning model, a decision tree, naive Bayes classification, a least square method, logistic regression, an integration method, a support vector machine, a clustering algorithm principal component analysis, singular value decomposition, independent component analysis and the like are introduced;

the second step is that: through the machine learning model selected in the first step and through the labeled training of the model, index classification and automatic discrimination under different brain health and cognitive function events and states are respectively carried out on EEG signal characteristics.

Preferably, in the fifth step, the pattern recognition and result judgment process of brain electrical deep learning:

the first step is as follows: for large-scale user monitoring and identification, along with the robustness requirement and the automatic updating requirement of an electroencephalogram signal identification algorithm, along with the increase of the user size and the increase of the data set scale, the traditional signal analysis and machine learning algorithm cannot meet the requirements on algorithm updating and automatic identification efficiency, and the design of an EEG electroencephalogram signal processing model of an artificial intelligence algorithm is developed;

the second step is that: end-to-end deep learning modeling and operation are carried out on a GPU high-performance server by introducing a deep learning neural network, training is carried out mainly on the basis of partial labeled EEG (electroencephalogram) data based on a cyclic neural network structure and a convolution upgrading network structure to form a classifier and a discriminator, and the classification is carried out on the EEG data

The third step: and meanwhile, data sets are continuously expanded, the neural network model algorithm is continuously updated, and the identification precision and accuracy of indexes of brain health, brain cognition and neural state are continuously improved.

Compared with the prior art, the invention has the beneficial effects that:

(1) according to the invention, after an original brain wire signal is portably collected by brain electricity, synchronous signal amplification is carried out, brain electricity signal transmission is carried out through wireless transmission, brain electricity signal receiving is carried out at a mobile terminal, the received signal is stored in cloud communication, signal identification is carried out through a brain electricity preprocessing algorithm, a brain point signal identification algorithm, a corresponding brain electricity machine learning algorithm and a deep learning algorithm, and measurement and evaluation of multiple indexes in the aspects of brain functions and brain mental health are carried out through analysis and calculation of collected data of brain signals of people in different types and states.

(2) The invention realizes the monitoring and the discrimination of the ability indexes of the cognitive function module of the brain in the aspects of emotion perception, attention concentration ability, depression state, anxiety state, neurasthenia, senile dementia, music perception, emotion perception, color perception, space perception, language cognition and the like, and the numerical value space representation discrimination interval of the corresponding index is generated according to the modeling of a signal recognition algorithm, a machine learning algorithm and a deep learning algorithm.

(3) The invention designs a universal portable EEG electroencephalogram acquisition device which can be integrated with various existing medical health care and entertainment devices, and utilizes a signal analysis algorithm, a machine learning algorithm and a deep learning algorithm to efficiently and intelligently sense emotion, attention focusing ability, depression state, anxiety state, neurasthenia, senile dementia, music sensing, emotion sensing, color sensing, space sensing, language sensing, brain energy consumption, brain anti-interference ability, brain inertia, brain sleepiness, brain arousal, endogenous anxiety, exogenous anxiety, brain fatigue, left-right brain symmetry, brain trauma, sleep disorder, personality disorder and brain convergence in brain according to different brain cognitive functions, brain health state and mental state events and state-related electroencephalogram characteristics according to corresponding electroencephalogram signals, monitoring and quantitative analysis of indexes such as brain inhibition, brain stability, memory processing ability, intracerebral concentration index, extracerebral concentration index, brain emptying index, brain reaction speed, brain absorption index, brain metabolism index and the like.

(4) The invention realizes the monitoring and the discrimination of the indexes of brain energy consumption, brain anti-interference capability, brain inertia, brain sleepiness, brain arousal, endogenous anxiety, exogenous anxiety, brain fatigue, left-right brain symmetry, brain trauma, sleep disorder, personality disorder and the like related to brain mental health, and generates the numerical value space representation discrimination interval of the corresponding index according to the signal recognition algorithm, the machine learning algorithm and the deep learning algorithm.

(5) The invention realizes the index monitoring and discrimination in the aspects of brain capability health, brain internal convergence, brain inhibition, brain stability, memory processing capability, brain internal concentration index, brain external concentration index, brain emptying index, brain reaction speed, brain absorption index, brain metabolism index and the like, and generates the numerical space representation discrimination interval of the corresponding index according to the signal recognition algorithm, the machine learning algorithm and the deep learning algorithm.

Drawings

FIG. 1 is a flow chart of the present invention;

FIG. 2 is a schematic diagram of signal acquisition and processing according to the present invention;

FIG. 3 is a schematic diagram of a signal identification algorithm flow chart of the present invention;

FIG. 4 is a schematic diagram of pattern recognition and result determination based on signal modal analysis according to the present invention;

FIG. 5 is a flow chart of a machine learning algorithm of the present invention;

FIG. 6 is a flow chart of the deep learning algorithm of the present invention;

FIG. 7 is a schematic diagram of the portable EEG apparatus of the present invention;

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1-7, the present invention provides a technical solution: a brain health screening method based on portable EEG equipment comprises an EEG signal acquisition and discrimination step, wherein the EEG signal acquisition and discrimination step comprises the following steps:

the first step is as follows: carrying out signal induction through a 1-4-lead high-sensitivity electrode plate, collecting original electroencephalogram signals, and carrying out signal stability test on the original signals through an impedance test module;

the second step is that: when the signals are stable, the original electroencephalogram signals are transmitted to an operational amplifier for signal amplification and signal intensity optimization;

a third part: the signals after being amplified and optimized are transmitted to a mobile terminal through a Bluetooth module, and the current physical state is judged through an acceleration sensor;

the fourth step: the mobile terminal transmits the received signals to a cloud high-performance computing server cluster for signal analysis and computation;

the fifth step: and the electroencephalogram signal mode recognition algorithm, the electroencephalogram machine learning algorithm and the electroencephalogram deep learning algorithm are used for carrying out calculation and judgment on related indexes such as brain health, brain function and the like on signals corresponding to electroencephalogram states and events.

In this embodiment, preferably, the signal acquisition and processing flow in the first step is as follows:

the first step is as follows: a user wears electroencephalogram monitoring equipment, and after a communication test is carried out, original electroencephalogram signals are transmitted and stored;

the second step is that: carrying out data preprocessing on the originally acquired signals, carrying out signal transformation through a Fourier algorithm, and converting the signals into digital signals;

the third step: noise removal and conversion change are carried out on the electroencephalogram signals converted into digital signals through a filtering algorithm and principal component analysis, and the removal is mainly carried out on the oculomotor interference, the electrocardio interference, the myoelectricity interference, the power frequency interference, the high-frequency noise interference and the like through a regression method, a self-adaptive filtering method and an independent component analysis method;

the fourth step: and uploading the original electroencephalogram signals subjected to noise removal to a storage server, and synchronously transmitting the original electroencephalogram signals to an electroencephalogram algorithm calculation server.

In this embodiment, preferably, the signal analysis and signal analysis calculation process in the fourth step is as follows:

the first step is as follows: extracting the characteristics of the original electroencephalogram signals, determining various parameters by taking the characteristic signals as source signals, and forming characteristic vector transformation representing the characteristics of the signals by taking the parameters as vectors;

the second step is that: performing time domain signal parameter extraction and transformation feature engineering on the original electroencephalogram signal vector features;

the third step: carrying out frequency domain signal parameter extraction and transformation characteristic engineering on the original electroencephalogram signal vector;

the fourth step: the signal characteristics and the electrode position relationship are used to classify the characteristics by using autoregressive, fourier transform, surface laplace transform, wavelet transform, and the like.

In this embodiment, preferably, in the fifth step, the electroencephalogram signal pattern recognition and result determination process:

the first step is as follows: in the initial stage, the electroencephalogram signals are analyzed in a time domain and a frequency domain, and feature extraction is carried out, from signal and noise identification of initial signal acquisition to filter algorithm setting, and through different parameter adaptations, the most effective signal-to-noise ratio signals acquired based on electroencephalogram EEG equipment are obtained;

the second step is that: the original effective electroencephalogram is obtained, and simultaneously, signal transformation, normalized frequency spectrum and power spectrum analysis, time sequence signal change fluctuation analysis, corresponding characteristic algorithm realization optimization adaptation are carried out, and measurement and judgment of indexes of brain health, brain function and mental state under the conditions of different events and related states are carried out.

In this embodiment, preferably, the pattern recognition and result judgment process of the learning of the brain-powered machine in the fifth step is as follows:

the first step is as follows: the extraction of the modeling characteristics of the EEG data processed by the signal analysis algorithm is realized, and meanwhile, algorithm models such as a machine learning model, a decision tree, naive Bayes classification, a least square method, logistic regression, an integration method, a support vector machine, a clustering algorithm principal component analysis, singular value decomposition, independent component analysis and the like are introduced;

the second step is that: through the machine learning model selected in the first step and through the labeled training of the model, index classification and automatic discrimination under different brain health and cognitive function events and states are respectively carried out on EEG signal characteristics.

In this embodiment, preferably, in the fifth step, the pattern recognition and result judgment process of the brain electrical deep learning:

the first step is as follows: for large-scale user monitoring and identification, along with the robustness requirement and the automatic updating requirement of an electroencephalogram signal identification algorithm, along with the increase of the user size and the increase of the data set scale, the traditional signal analysis and machine learning algorithm cannot meet the requirements on algorithm updating and automatic identification efficiency, and the design of an EEG electroencephalogram signal processing model of an artificial intelligence algorithm is developed;

the second step is that: end-to-end deep learning modeling and operation are carried out on a GPU high-performance server by introducing a deep learning neural network, training is carried out mainly on the basis of partial labeled EEG (electroencephalogram) data based on a cyclic neural network structure and a convolution upgrading network structure to form a classifier and a discriminator, and the classification is carried out on the EEG data

The third step: and meanwhile, data sets are continuously expanded, the neural network model algorithm is continuously updated, and the identification precision and accuracy of indexes of brain health, brain cognition and neural state are continuously improved.

Reference ranges for emotional perception in the present invention: feeling of emotion: reference ranges: the strong range of emotional perception is more than 120; the normal range of emotional perception is [50-120 ]; the weak range of emotional perception is < 60.

Attention capacity: reference ranges: the strong range of the attention control force is less than 60; the normal range of attention control is [60-90 ]; the weak range of attention control is > 90.

Depressed state: reference ranges: the normal good mental state with no signs of depression was 80; there was a mild abnormality in normal mental status with mild signs of depression ranging from [40-80 ]; there was an abnormality in mental state with signs of mild and above depression 40.

Anxiety states: reference ranges: the mental state is normal and good, and the symptom of no anxiety is 5; the mental state is slightly different, and the reference range of mild anxiety is [5-40 ]; there was an abnormality in mental state with the reference range of 40 for mild plus signs of anxiety

Neurasthenia: reference ranges: the mental state is normal and good, and the reference range of no neurasthenia sign is less than 80; there was a slight abnormality in mental state with a reference range of [80-120] for mild signs of neurasthenia; the mental state is abnormal, and the reference range of the neurasthenia with more than mild degree is more than 120

Senile dementia: reference ranges: good, no senile dementia sign reference range is 0-120; normal, the reference range for the presence of the evidence of senile dementia with a smaller probability is [ 120-; there was evidence of senile dementia 140

Brain energy consumption: reference ranges: 130-300;

brain chaos: reference ranges: 0 to 7;

brain inertia: reference ranges: 190-;

sleepiness of the brain: reference ranges: 0 to 20;

brain alertness: reference ranges: 0 to 15;

endogenous anxiety: reference ranges: 0 to 20;

brain fatigue: reference ranges: 0 to 20;

left and right lateral brains: reference ranges: 80-120 parts of;

brain convergence: reference ranges: 30-55 parts of;

brain inhibition: reference ranges: 35-65 parts of;

brain stabilization: reference ranges: 45-70 parts of;

memory processing: reference ranges: 3-10;

internal concentration: reference ranges: 0 to 30;

focus on the outside: reference ranges: 0 to 15;

brain emptying: reference ranges: 10-70 parts of;

reaction speed: reference ranges: 5-15;

although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.