• 专利附录
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    • 专利摘要:
    Procedure for estimating brain noise through ambiguous images that includes the following steps: - show an ambiguous image to a subject; - apply a continuous change during a time T to the control parameter responsible for the change in the perception of two interpretations of said image, - record the brain activity of said subject through a magneto-encephalography; - calculate the time τ using a spectral analysis; between two interpretations of the image; - represent the curve τ/T with respect to T to obtain the value N in which the curve becomes saturated. (Machine-translation by Google Translate, not legally binding)
    公开号:ES2639644A1
    申请号:ES201630575
    申请日:2016-05-04
    公开日:2017-10-27
    发明作者:Alexander Pisarchik；Rider JAIMES REÁTEGUI
    申请人:UNIVERSIDAD DE GUADALAJARA；Universidad Politecnica de Madrid；Univ Guadalajara；
    IPC主号:
    专利说明:

    Procedure for estimating brain noise through ambiguous images.
    5 SECTOR OF THE TECHNIQUEThe following invention is related to the field of brain understanding, more inparticular with the understanding of the states related to brain noise. STATE OF THE TECHNIQUE
    10 The biological processes and mechanisms underlying the change between different visual perceptions are the subject of modern research. According to the most recent models, the biological origin of the perceptual changes would be found in the brain noise that is generated due to the randomness in the spontaneous activity of the neuronal cells and that is inherent in the cognitive function of the brain.
    15 Brain noise plays an important role in processing stimuli for decision making, allowing different interpretations of a single image. Classic examples of ambiguous images are the rabbit / duck, the Rubin Vase, the Necker cube and the horizontally rotated transparent sphere. The interpretation of ambiguous images
    20 has a probabilistic character, characterized by the duration times of each assessment of an ambiguous image and the frequency of changes between different assessments. The level of brain noise is therefore an important indicator to determine cognitive problems and diagnose some mental illnesses.
    The patent application US2014107521 (A1) is related to the use of biomarkers to predict the existence of diseases, such as epilepsy or autism, according to the functional connectivity of the brain and its background noise. The procedure described in this application, however, does not allow direct measurement of effective brain noise.
    OBJECT OF THE INVENTION The main objective of the present invention is to propose a method of estimating noise inherent in the cognitive function of the brain through ambiguous images that solves the problems described above. For this, the invention comprises the steps of: showing an ambiguous image to a subject, applying a continuous change for a time T to
    35 control parameter responsible for the change in the perception of two interpretations of said image, record the brain activity of said subject through a magnet


    Encephalography, calculate by means of a spectral analysis the time mediante between two interpretations of the image and represent the curve  / T with respect to T to obtain the N value in which the curve is saturated. In an example of implementation, the ambiguous image is the Necker cube and the control parameter is the contrast between edges of the front and back sides. In another example, the ambiguous image is the horizontally rotated sphere and the control parameter is the difference in illumination of the pixels of the front and back faces. BRIEF DESCRIPTION OF THE FIGURES
    In order to help a better understanding of the features of the invention in accordance with a preferred example of practical realization thereof, the following description of a set of drawings is attached, where the following has been represented by way of illustration:
    Figure 1 is a flow chart of the process of the invention.
    Figure 2 is a representation of the technique of applying labels to ambiguous images, as an example, the Necker cube is used.
    Figure 3 is a spectrum of frequencies obtained when the subject observes the central image of Figure 2.
    Figure 4 is a graph of the spectral changes in magneto-encephalography of a subject during the contrast variation in Figure 2.
    Figure 5 is a graph of saturation N for three different subjects. DETAILED DESCRIPTION OF THE INVENTION
    The method of the invention allows measuring the level of noise present in the brain and inherent in cognitive function. For this it makes use of ambiguous images and the time it takes for the brain to change their interpretation, measured thanks to a spectral analysis through magneto-encephalography (MEG).
    Visual perception begins with the conversion of electromagnetic energy packets into a signal that can be analyzed by the brain. This conversion is carried out by the photoreceptor cells of the eye, a set of specialized cells that are located in the retina. The visual stimulus that a person receives activates brain neurons. The brain interprets the stimuli received according to the previous knowledge and experience of that person. If several interpretations of the same coexist


    stimulus, neurons respond differently. The brain needs a certain time to be able to discern each interpretation. It is not possible for the brain to simultaneously interpret the same stimulus in different ways, just as it is not possible for a neuron to be simultaneously in different states, for example, excitation and rest.
    The method of the invention is shown in Figure 1. The brain activity of the subject is detected through magneto-encephalography (MEG) and the spectral analysis of said encephalography allows measuring the change times in the interpretation of the ambiguous image, which depend on the rate of change of the control parameter.
    The procedure uses an ambiguous image that can be interpreted by a subject in two different ways. First, a control parameter responsible for the interpretation of the image is sought and a continuous change is applied to this parameter so that the subject changes his appreciation of said image. Simultaneously, the subject's brain activity is observed through the MEG.
    In the case of the Necker cube shown in Figure 2, the contrast of the inner edges of the cube, responsible for the orientation of the faces, is used as a control parameter. The contrast of three inner edges in the left image is decreased continuously, while the contrast of three inner edges of the right image increases. When the inner edges of the cube have the same contrast (as in the central image of Figure 2), the positions of the orientations of the R and A faces can be interpreted by a subject in two different ways: the R face can be seen in front as in the left image or face A can be seen in front as the right image. When the contrast of three inner edges of the left side cube decreases continuously, while the contrast of three inner edges of the right side cube increases simultaneously, the image of the cube is transformed from the cube on the left side to the cube on the right side . The subject who is observing this transformation sees a change in the position of the R and A faces at a time of time  that depends on the speed of the contrast change and the internal noise of the brain.
    In order to detect the change in the interpretation of the image through the MEG, a random modulation (external noise) is applied to the lighting of the image pixels, (this step is known as label application), with different frequencies for each part of the image associated with different appreciation and the components are measured


    spectral of the cortical signals recorded by the MEG at the frequencies of the corresponding tags. The dynamic noise used distorts the image only minimally, both perceptions and their spontaneous switching are maintained.
    When the control parameter changes continuously, the subject identifies a change in the image at a certain moment of time  that depends on the speed of the change of the control parameter. The mechanisms that underlie changes between different visual perceptions are found in noise, inherent in the activity of the brain's neuronal cells, which results in the spontaneous random activation of individual neurons. Thus, the change between different interpretations is a stochastic process, where each particular interpretation has a certain probability that depends on brain noise.
    The duration of each interpretation of a stimulus depends on the brain noise of the person. The higher the noise level, the shorter the duration intervals of each interpretation. Without noise, no change in decision regarding the same stimulus would be possible. In addition, the interpretation time intervals depend on the parameter responsible for the interpretation of an ambiguous image. As will be seen later, in the Necker cube is the contrast of the edges, in the rotating sphere it is the illumination of the pixels on the surfaces.
    The response time of brain activity to visual stimuli (cognitive time) detected by the MEG is approximately 150-250 ms. Due to the bifurcation slowing effect near the critical point, when the control parameter (the contrast of the edges in the case of the Necker cube) changes over time, the brain cannot react instantaneously and therefore the subject takes time your answer. The decrease in speed (the increase in the time of changing the contrast T) levels the slowing effect because the control parameter does not change much during the time necessary for the subject to make a decision, which in turn results in saturation from
    the normalized response times T as shown in Figure 5. In this case, the normalized time for changing the orientation of the Necker N cube is determined only by brain noise.
    The spectral analysis of the MEG shows the times cambio of the change of the spectral components in the frequencies of the tags (figure 4), associated with the change of interpretation of the image. The MEG data is recorded with different T times of the change of the control parameter and the normalized times are plotted


    T as a function of the duration T of the change of the control parameter. With the increase in T, this dependence becomes saturated at an N value related to brain noise.
    In a particular implementation in which the Necker cube is used, to be able to measure the time  through the MEG when the ratio between the powers of the spectral components in the frequencies of the tags is rapidly changed (Figure 2) , two labels with the same intensity value and frequencies of appearance 15 and 10 Hertz are used, which are applied to the front and rear faces R and A respectively. A spectral analysis of the MEG allows the different spectral components of the signals emitted by the brain to be plotted over time, as shown in Figure 3.
    When the subject identifies the change in the image, the MEG spectrum shows a change in the spectral components at frequencies 15 and 10 Hertz, as seen in Figure 4. At the moment when the subject changes the perception, produces a jump in the value of the spectral components of each label; One component increases while the other decreases. This change occurs over time  which depends on the speed of the contrast change.
    The values of  for each subject are obtained through the MEGs recorded for different times T of contrast change (Figure 4). Then, to obtain N, the normalized times  / T are plotted as a function of T, as shown in Figure 5. When the contrast changes slowly (T> 20 seconds), the ratio  / T almost does not depend on T and is determined only by the internal noise of the brain. The saturation value N in Figure 5 is directly related to the effective noise of the brain. It is a relative parameter that influences the decision of the interpretation of the image of each individual determined by the cognitive stochastic processes in their brain. The normalized value  / T has no dimension and the noise of different subjects can be compared by comparing their values of N. For example, subject 1 has the loudest noise that subjects 2 and 3, N1> N2 ≈ N3. This may mean that subject 1 is more flexible in their decisions, but cannot concentrate as subjects 2 and 3.
    In another example of implementation of the invention a sphere rotated horizontally on both sides is used. The sphere has the pixels on both sides, front and back rotating horizontally in different directions. If this sphere is observed for a long time it seems that the sphere changes the direction of rotation. The


    The difference between lighting of the two-sided pixels is in this case the control parameter. When the illumination of the pixels on the front side increases, simultaneously the illumination of the pixels on the rear side decreases. This induces a change in the probability in the direction of rotation of the sphere. External noise tags are
    5 placed in the same way as in the case of the Necker cube. That is, the pixels on the front side are marked with a noise of 15 Hz, while the pixels on the back side are marked with 10 Hz. The results are consistent with the test performed with the Necker cube.

    权利要求:
    Claims (2)
    [1]
    1. Procedure for estimating brain noise through ambiguous images comprising the following steps:
    5 -show an ambiguous image to a subject; -apply a continuous change during a time T to the control parameter responsible for the change in the perception of two interpretations of said image; -register the brain activity of said subject through magneto-encephalography; -calculate the time between two interpretations of the image by means of a spectral analysis;
    10 -represent the  / T curve with respect to T to obtain the N value at which the curve is saturated.
    [2]
    2. The method according to claim 1 wherein the ambiguous image is the Necker cube and the control parameter is the contrast between edges of the front face and the back face.
    The method according to claim 1 wherein the ambiguous image is the horizontally rotated sphere and the control parameter is the difference in illumination of the pixels of the front and back faces.

     FIG. 2 
     Hz0.98-660-270-280-290-300-310-320
    -3305 101520253035404550
    Frequency (Hz)
    FIG. 3
    Power (dB / Hz)

    Time (300ms)
    FIG. 4

    0.3
    0.2
    0.1
    0 5 1015202530
    FIG. 5
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