阅读说明：本技术 一种基于脑电信息的脑区发育评估及功能评估分析方法 (Brain area development evaluation and function evaluation analysis method based on electroencephalogram information ) 是由 褚明礼 于 2021-01-15 设计创作，主要内容包括：本发明提供了一种基于脑电信息的脑区发育评估及功能评估分析方法,该方法包括如下步骤：多点位获取用户脑电信号；对所述脑电信号进行处理以获得多个预设波段的功率；将各个点位重新组合,划分为脑功能区特征点位和脑发育特征点位；分别构建所述脑功能区特征点位和所述脑发育特征点位的评价模型；根据所述预设波段的功率,获得所述评价模型的得分并输出；根据评价结果,给出用户处于的脑区发育级别。(The invention provides a brain area development evaluation and function evaluation analysis method based on electroencephalogram information, which comprises the following steps: acquiring user electroencephalogram signals at multiple points; processing the electroencephalogram signals to obtain power of a plurality of preset wave bands; recombining all the point locations to divide the point locations into feature point locations of brain functional areas and feature point locations of brain development; respectively constructing evaluation models of the feature point of the brain functional region and the feature point of the brain development; obtaining and outputting the score of the evaluation model according to the power of the preset wave band; and according to the evaluation result, giving the brain region development level of the user.)

1. A brain area development evaluation and function evaluation analysis method based on electroencephalogram information is characterized by comprising the following steps:

acquiring user electroencephalogram signals at multiple points;

processing the electroencephalogram signals to obtain power of a plurality of preset wave bands;

recombining all the point locations to divide the point locations into feature point locations of brain functional areas and feature point locations of brain development;

respectively constructing evaluation models of the feature point of the brain functional region and the feature point of the brain development;

and obtaining and outputting the score of the evaluation model according to the power of the preset wave band.

2. The brain area development evaluation and function evaluation analysis method based on electroencephalogram information of claim 1, wherein electrodes are placed according to electrode point positions marked by the international 10-20 system method when the electroencephalogram signals of the user are acquired.

3. The brain area development evaluation and function evaluation analysis method based on electroencephalogram information, according to claim 2, wherein the preset wave bands comprise delta waves, theta waves, alpha waves, SMR waves, beta waves and H beta waves, and the total number of the preset wave bands is 6.

4. The brain area development evaluation and function evaluation analysis method based on electroencephalogram information as claimed in claim 3, wherein each point is divided by adopting a brain template, or an independent component analysis method, or a priori region information method, so that the brain area is divided into different brain functional area characteristic point locations and brain development characteristic point locations.

5. The brain area development evaluation and function evaluation analysis method based on electroencephalogram information of claim 4, wherein the brain function area feature point comprises:

attention index points including F4, F3, Fp1, Fp 2;

the multi-kinetic index points comprise C3, C4, F3 and F4;

mood index points including Fp2, F4, F8, T4;

sensory integration index points including C3, C4, P3, P4;

sleep quality index points including P3, P4, O1, O2;

speech development index points including F7, F3, T3, T5, P3;

executing function points including Fp1, Fp2, F7, F3, F4 and F8;

fatigue index points including P1, Fp2, F7, F3, F4, F8, P3, P4, O1 and O2;

and the intelligence development index points comprise 01 and O2.

6. The brain area development evaluation and function evaluation analysis method based on electroencephalogram information of claim 5, wherein the brain development feature points comprise:

left frontal lobe developmental index points including Fp1, F3, F7;

right frontal lobe developmental index points including Fp2, F4, F8;

apical leaf development index points including P3, P4;

median band development index points including C3, C4;

occipital lobe development index points including O1, O2;

left temporal lobe developmental index points, including T3, T5;

right temporal lobe developmental index points, including T4, T6;

the whole brain development index points comprise P3, P4, O1, O2, Fp1, Fp2, F3, F4, C3, C4, T3, T5, T4, T6, F7 and F8.

7. The brain area development evaluation and function evaluation analysis method based on electroencephalogram information according to claim 6, wherein the constructed evaluation model of the feature points of the brain function areas comprises:

total index of the attention index point location: AI ═ α/θ (H β/δ);

the total index of the multi-action index point location is as follows: HI δ/SMR;

the total index of the sentiment index points: EI ═ H β/β;

total index of the sensory integration index points: SII ═ α/θ;

the total index of the sleep quality index point location: SI ═ (α × β)/(θ × H β);

the total index of the speech development index points: SDI ═ (. alpha./θ)/(. beta./δ);

an index EAI ═ α/θ ═ β/δ)/(H β/β of the executive function point;

the total index of the fatigue index point location: FI is SMR/alpha;

the intelligence development index point shows the dominant frequency advantages of O1 and O2.

8. The brain area development evaluation and function evaluation analysis method based on electroencephalogram information according to claim 7, wherein the evaluation model of the brain development feature point location, which is constructed, comprises:

the overall index of the left frontal lobe development index point LFDI ═ AVERAGE (LFDI (Fp1), LFDI (F3), LFDI (F7)), wherein the point index LFDI (Fp1) ═ a (Fp1), LFDI (F3) ═ a (F3), LFDI (F7) ═ a (F7);

the overall index of the right frontal lobe development index point, RFDI ═ AVERAGE (RFDI (Fp2), RFDI (F4), RFDI (F8)), where the point index: RFDI (Fp1) ═ a (Fp2), RFDI (F3) ═ a (F4), RFDI (F7) ═ a (F8);

the overall index PDI of the apical leaf development index point is AVERAGE (PDI (P3), PDI (P4)), where the point index: PDI (P3) ═ a (P3), PDI (P4) ═ a (P4);

the central band development index point has a total index CDI ═ AVERAGE (CDI (C3), CDI (C4)), where the point index: CDI (C3) ═ a (C3), CDI (C4) ═ a (C4);

the overall index of occipital lobe development index point, ODI ═ AVERAGE (ODI (O1), ODI (O2)), where the point index: ODI (O1) ═ a (O1), ODI (O2) ═ a (O2);

the overall index LTDI ═ AVERAGE (LTDI (T3), LTDI (T5)) for the left temporal lobe developmental index point, where the point index: LTDI (T3) ═ a (T3), LTDI (T5) ═ a (T5);

the overall index of the right temporal lobe development index point RTDI ═ AVERAGE (RTDI (T4), RTDI (T6)), where the point index: RTDI (T4) ═ a (T4), RTDI (T6) ═ a (T6);

the overall index ADI of the global brain development index point (PDI, ODI, FDI, CDI, LTDI, RTDI), wherein the midpoint index: PDI, ODI, LFDI, RFDI, CDI, LTDI, RTDI;

wherein, the factor A is alpha/theta.

Technical Field

The invention relates to the technical field of electroencephalogram monitoring and evaluation, in particular to a brain area development evaluation and function evaluation analysis method based on electroencephalogram information.

Background

At present, the comprehensive research of multiple levels on high-level cognitive functions such as thinking, learning, language and attention of human brain becomes one of the hot directions of modern scientific development, and the electroencephalogram signals collected from human scalp increasingly become indispensable experimental and analytical means in the research due to the unique attributes of the electroencephalogram signals, so that the analysis and processing of the electroencephalogram signals become an indispensable content in the research of brain science.

Brodmann, a neurologist of germany, in 1909 divided cerebral cortex into 52 regions, called brodmann regions, based on similarities in cortical cell structure-density, cell shape, cell size, etc. Such partitioning is of great significance, and sensory and motor functions can be generally divided into three levels, primary, secondary and advanced, each level belonging to a different part from the viewpoint of brodman partitioning. In general, the primary cortical region responds only to a specific sensory secondary, and the secondary cortical region is linked to the primary cortical region, and processing the information conveyed by a specific sensory channel, damage to the secondary cortical region will lead to perceptual impairment. The high-grade cortical areas are generally located in the range enclosed by the boundaries of each secondary cortical areas of the parietal lobe, the frontal lobe, the temporal lobe and the occipital lobe and are overlapped areas of the parietal lobe, the temporal lobe and the occipital lobe, various sensory information are integrated into high-grade cognition in the areas, and the cognitive impairment can be caused in the areas.

The electroencephalogram is a modern auxiliary examination method which uses an electroencephalograph to record weak bioelectricity of the brain into a curve to help diagnose diseases, has no any wound on an examined person and has certain diagnostic value on the brain diseases. Conventional electroencephalography refers to the electroencephalogram of the scalp traced in a prescribed uniform manner and time under normal physiological conditions and in a quiet and comfortable state. The standard electrode placement method (international 10/20 system method) proposed by the international electroencephalogram society is the most clinically applied at present, wherein FP is frontal polar, Z represents a midline electrode, FZ is frontal, CZ is a central point, PZ is a vertex, O is a occipital point, T is a temporal point, and a is an earlobe electrode.

In the prior art, different brain waves can be presumably linked to different cognitive activities. For example:

alpha waves are more active at rest; beta waves are more active at high concentration and concentration; brain waves with poor nervous system regulation capability are mostly represented by excessive electroencephalogram power density of Theta waves (4-8Hz) and high Beta waves (18-36Hz), poor stability of SMR waves (12-15Hz) and insufficient electroencephalogram power density. Theta waves are usually the dominant force band of sleep stages in the nervous system, Delta waves are more active in deep sleep; however, if the Theta band is too high in the wake-up period, the human is often in a brain inhibition state, which is mostly shown in dull, vague, somnolence and more obvious imagination; the excessive intensity of the high Beta wave band indicates that the nerve activity is easy to generate impulsion, generate spleen qi, tension and anxiety, and easily generate fatigue and the like.

According to the above, attention, emotion, sleep quality, intelligence development and work efficiency of people are closely related to electroencephalogram activity, but no related technology exists in the prior art, so that the development condition of a brain function area is reflected through electroencephalogram information.

Therefore, how to develop a brain development assessment and function assessment analysis method based on electroencephalogram information, and to assess the cognitive development level and brain development process of a user by detecting the electroencephalogram information becomes a technical problem which needs to be solved at present.

Disclosure of Invention

In order to solve the above problems in the prior art, the present invention provides a brain area development assessment and function assessment analysis method based on electroencephalogram information, which performs accurate quantitative assessment of functions of the whole brain by collecting electroencephalogram information, so as to assess the functions and development of different brain areas of a user.

In order to achieve the purpose, the invention provides the following technical scheme:

a brain area development evaluation and function evaluation analysis method based on electroencephalogram information comprises the following steps:

acquiring user electroencephalogram signals at multiple points;

processing the electroencephalogram signals to obtain power of a plurality of preset wave bands;

recombining all the point locations to divide the point locations into feature point locations of brain functional areas and feature point locations of brain development;

respectively constructing evaluation models of the feature point of the brain functional region and the feature point of the brain development;

and obtaining and outputting the score of the evaluation model according to the power of the preset wave band.

Preferably, when the user electroencephalogram signal is acquired, an electrode is placed according to an electrode point position marked by the international 10-20 system method.

Preferably, the preset wavelength band includes 6 wavelength bands in total, including a delta wave, a theta wave, an alpha wave, an SMR wave, a beta wave, and an H beta wave.

Preferably, the brain template or the independent component analysis method or the prior region information based method is adopted to divide each point location, and the brain area is divided into different brain functional area characteristic point locations and brain development characteristic point locations.

Preferably, the functional brain region feature points include:

attention index points including F4, F3, Fp1, Fp 2;

the multi-kinetic index points comprise C3, C4, F3 and F4;

mood index points including Fp2, F4, F8, T4;

sensory integration index points including C3, C4, P3, P4;

sleep quality index points including P3, P4, O1, O2;

speech development index points including F7, F3, T3, T5, P3;

executing function points including Fp1, Fp2, F7, F3, F4 and F8;

fatigue index points including P1, Fp2, F7, F3, F4, F8, P3, P4, O1 and O2;

and the intelligence development index points comprise 01 and O2.

Preferably, the brain development feature points include:

left frontal lobe developmental index points including Fp1, F3, F7;

right frontal lobe developmental index points including Fp2, F4, F8;

apical leaf development index points including P3, P4;

median band development index points including C3, C4;

occipital lobe development index points including O1, O2;

left temporal lobe developmental index points, including T3, T5;

right temporal lobe developmental index points, including T4, T6;

the whole brain development index points comprise P3, P4, O1, O2, Fp1, Fp2, F3, F4, C3, C4, T3, T5, T4, T6, F7 and F8.

Preferably, the constructed evaluation model of the feature points of the functional brain region comprises:

total index of the attention index point location: AI ═ α/θ (H β/δ);

the total index of the multi-action index point location is as follows: HI δ/SMR;

the total index of the sentiment index points: EI ═ H β/β;

total index of the sensory integration index points: SII ═ α/θ;

the total index of the sleep quality index point location: SI ═ (α × β)/(θ × H β);

the total index of the speech development index points: SDI ═ (. alpha./θ)/(. beta./δ);

an index EAI ═ α/θ ═ β/δ)/(H β/β of the executive function point;

the total index of the fatigue index point location: FI is SMR/alpha;

the intelligence development index point shows the dominant frequency advantages of O1 and O2.

Preferably, the evaluation model of the brain development feature point site is constructed by the following steps:

the overall index of the left frontal lobe development index point LFDI ═ AVERAGE (LFDI (Fp1), LFDI (F3), LFDI (F7)), wherein the point index LFDI (Fp1) ═ a (Fp1), LFDI (F3) ═ a (F3), LFDI (F7) ═ a (F7);

the overall index of the right frontal lobe development index point, RFDI ═ AVERAGE (RFDI (Fp2), RFDI (F4), RFDI (F8)), where the point index: RFDI (Fp1) ═ a (Fp2), RFDI (F3) ═ a (F4), RFDI (F7) ═ a (F8);

the overall index PDI of the apical leaf development index point is AVERAGE (PDI (P3), PDI (P4)), where the point index: PDI (P3) ═ a (P3), PDI (P4) ═ a (P4);

the central band development index point has a total index CDI ═ AVERAGE (CDI (C3), CDI (C4)), where the point index: CDI (C3) ═ a (C3), CDI (C4) ═ a (C4);

the overall index of occipital lobe development index point, ODI ═ AVERAGE (ODI (O1), ODI (O2)), where the point index: ODI (O1) ═ a (O1), ODI (O2) ═ a (O2);

the overall index LTDI ═ AVERAGE (LTDI (T3), LTDI (T5)) for the left temporal lobe developmental index point, where the point index: LTDI (T3) ═ a (T3), LTDI (T5) ═ a (T5);

the overall index of the right temporal lobe development index point RTDI ═ AVERAGE (RTDI (T4), RTDI (T6)), where the point index: RTDI (T4) ═ a (T4), RTDI (T6) ═ a (T6);

the overall index ADI of the global brain development index point (PDI, ODI, FDI, CDI, LTDI, RTDI), wherein the midpoint index: PDI, ODI, LFDI, RFDI, CDI, LTDI, RTDI;

wherein, the factor A is alpha/theta.

Compared with the prior art, the brain area development evaluation and function evaluation analysis method based on the electroencephalogram information achieves the following effects:

(1) and electrodes are placed in a partition mode according to the electrode positions marked by the international 10-20 system method and the Brodeman function, so that the acquired electroencephalogram signals can accurately quantify and evaluate the whole brain function more accurately and reasonably.

(2) The method comprises the steps of collecting 6 wave bands of electroencephalogram signals, constructing an evaluation model based on brain functional area characteristic point and brain development characteristic point, and enabling evaluation of brain development and brain functions of a user to be more accurate by calculating power ratio of each wave band.

(3) And taking points based on the Brodeman functional partition and the 10-20 international electroencephalogram standard to subdivide the characteristic points, so that the development evaluation and quantification indexes of different brain areas can be accurately judged by constructing an electroencephalogram model.

(4) The problems that the traditional graph reading is difficult and the requirement on graph reading personnel is high are solved, so that various indexes can be accurately quantized, and evaluation is easy to grade according to calculation indexes.

Drawings

Fig. 1 is a block flow diagram of a preferred embodiment of a brain area development evaluation and function evaluation analysis method based on electroencephalogram information in the present invention.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.

Referring to fig. 1, in a preferred embodiment, a brain development assessment and function assessment analysis method based on electroencephalogram information is disclosed, which includes the following steps:

step S11, acquiring user electroencephalogram signals in multiple points;

step S12, processing the electroencephalogram signals to obtain power of a plurality of preset wave bands;

step S13, recombining all the point locations to divide the point locations into a brain functional area characteristic point location and a brain development characteristic point location;

step S14, respectively constructing an evaluation model of the brain functional region characteristic point and the brain development characteristic point;

step S15, obtaining and outputting the score of the evaluation model according to the power of the preset wave band;

in the following, the brain region development evaluation and function evaluation analysis method based on electroencephalogram information in the present exemplary embodiment will be further described:

in step S11, when acquiring the user electroencephalogram, placing an electrode according to an electrode point position marked by the international 10-20 system method; specifically, Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5 and T6 can be selected, and each point acquisition waveband is 0-30 Hz original waveband.

In step S12, the acquired electroencephalogram signals are amplified and filtered to divide the bands into δ (0.8 to 4Hz), θ (4 to 8Hz), α (8 to 12Hz), SMR (12 to 15Hz), β (15 to 20Hz), and hbeta (20 to 30 Hz).

In step S13, each point is divided by using a brain template, an independent component analysis method, or a priori region information method, and the brain region is divided into different brain functional region feature points and brain development feature points according to brodman partition and function.

Wherein, the brain functional region feature point location includes:

attention index points, the reference points including F4, F3, Fp1, Fp 2;

the index points of hyperactivity include C3, C4, F3 and F4;

emotional index points, wherein the reference points comprise Fp2, F4, F8 and T4;

sensory synthesis index points, wherein the reference points comprise C3, C4, P3 and P4;

the sleep quality index points comprise P3, P4, O1 and O2;

speech development index points, the reference points including F7, F3, T3, T5, P3;

executing function points, wherein the reference points comprise Fp1, Fp2, F7, F3, F4 and F8;

fatigue index points, wherein the reference points comprise P1, Fp2, F7, F3, F4, F8, P3, P4, O1 and O2;

intelligence development index points, and the reference points comprise 01 and O2.

Wherein the brain development feature points comprise:

left frontal lobe development index points, the reference points including Fp1, F3, F7;

right frontal lobe development index points, the reference points including Fp2, F4, F8;

apical leaf development index points, the reference points including P3, P4;

the development index points of the central zone, and the reference points comprise C3 and C4;

occipital lobe development index points, wherein the reference points comprise O1 and O2;

left temporal lobe developmental index points, reference points including T3, T5;

right temporal lobe developmental index points, reference points including T4, T6;

and the whole brain development index points, wherein the reference points comprise P3, P4, O1, O2, Fp1, Fp2, F3, F4, C3, C4, T3, T5, T4, T6, F7 and F8.

In step S14, the constructed evaluation model of the feature points of the functional brain region includes:

total index of the attention index point location: AI ═ α/θ (H β/δ), referenced in the range 0.67-2.22-3.56;

the total index of the multi-action index point location is as follows: HI δ/SMR, referenced range 3.99-8.6-15;

the total index of the sentiment index points: EI ═ H β/β, reference range 4.89-3.81-2.76;

total index of the sensory integration index points: SII ═ α/θ, reference range 0.52-1.57-1.96;

the total index of the sleep quality index point location: SI ═ (α × β)/(θ × H β), reference range 1.01-3.55-8.01;

the total index of the speech development index points: SDI ═ (α/θ)/(β/δ), referenced ranges 0.89-3.68-9.38;

the index EAI of the executive function point is (α/θ) × (β/δ)/(H β/β), with a reference range of 0.73-2.2-3.53;

the total index of the fatigue index point location: FI ═ SMR/α, reference range 0.78-1.57-2.61;

the intelligence development index point shows the dominant frequency advantages of O1 and O2, and the reference range is 8.5-10-11.5.

Further, the constructed evaluation model of the brain development feature point location comprises:

the overall index of the left frontal lobe development index point LFDI ═ AVERAGE (LFDI (Fp1), LFDI (F3), LFDI (F7)), wherein the point index LFDI (Fp1) ═ a (Fp1), LFDI (F3) ═ a (F3), LFDI (F7) ═ a (F7), the reference range is 0.56-1.57-2.04;

the overall index of the right frontal lobe development index point, RFDI ═ AVERAGE (RFDI (Fp2), RFDI (F4), RFDI (F8)), where the point index: RFDI (Fp1) ═ a (Fp2), RFDI (F3) ═ a (F4), RFDI (F7) ═ a (F8), the reference range is 0.56-1.57-2.04;

the overall index PDI of the apical leaf development index point is AVERAGE (PDI (P3), PDI (P4)), where the point index: PDI (P3) ═ a (P3), PDI (P4) ═ a (P4), reference ranges from 0.47 to 1.23 to 1.58;

the central band development index point has a total index CDI ═ AVERAGE (CDI (C3), CDI (C4)), where the point index: CDI (C3) ═ a (C3), CDI (C4) ═ a (C4), reference ranges from 0.65-1.83-2.65;

the overall index of occipital lobe development index point, ODI ═ AVERAGE (ODI (O1), ODI (O2)), where the point index: ODI (O1) ═ a (O1), ODI (O2) ═ a (O2), reference ranges from 0.61 to 1.24 to 2.29;

the overall index LTDI ═ AVERAGE (LTDI (T3), LTDI (T5)) for the left temporal lobe developmental index point, where the point index: LTDI (T3) ═ a (T3), LTDI (T5) ═ a (T5), reference ranges from 0.52-1.44-1.85;

the overall index of the right temporal lobe development index point RTDI ═ AVERAGE (RTDI (T4), RTDI (T6)), where the point index: RTDI (T4) ═ a (T4), RTDI (T6) ═ a (T6), reference ranges from 0.52-1.36-1.85;

the overall index ADI of the global brain development index point (PDI, ODI, FDI, CDI, LTDI, RTDI), wherein the midpoint index: PDI, ODI, LFDI, RFDI, CDI, LTDI, RTDI, with a reference range of 0.55-1.61-2.05.

In the above model, the factor a is α/θ.

And comparing the scores of the evaluation models with the reference range of the data to evaluate the development and functional development conditions of the brain region.

In a preferred embodiment, the method further comprises a step S16 of giving the brain region development level of the user according to the evaluation result, as shown in the following table:

compared with the prior art, the invention provides a brain area development evaluation and function evaluation analysis method based on electroencephalogram information, which achieves the following effects:

(1) and electrodes are placed in a partition mode according to the electrode positions marked by the international 10-20 system method and the Brodeman function, so that the acquired electroencephalogram signals can accurately quantify and evaluate the whole brain function more accurately and reasonably.

(2) The method comprises the steps of collecting 6 wave bands of electroencephalogram signals, constructing an evaluation model based on brain functional area characteristic point and brain development characteristic point, and enabling evaluation of brain development and brain functions of a user to be more accurate by calculating power ratio of each wave band.

(3) And taking points based on the Brodeman functional partition and the 10-20 international electroencephalogram standard to subdivide the characteristic points, so that the development evaluation and quantification indexes of different brain areas can be accurately judged by constructing an electroencephalogram model.

(4) The problems that the traditional graph reading is difficult and the requirement on graph reading personnel is high are solved, so that various indexes can be accurately quantized, and evaluation is easy to grade according to calculation indexes.

The foregoing description shows and describes several preferred embodiments of the present application, but as aforementioned, it is to be understood that the application is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the application as described herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the application, which is to be protected by the claims appended hereto.