Methods and devices for guiding neural stimulation

Abstract

According to the present disclosure, there is provided a novel method for improving a user's cognitive performance, comprising: M ) to the user, each session requiring one or more responses from the user. s A method is provided, comprising: visual training tasks configured to induce an increase in brain frequency power based on a user's response to at least one visual training task displayed to the user.

Description

[Technical Field]

[0001] The present disclosure relates to methods and devices for guiding neural stimulation. [Background technology]

[0002] Gamma-frequency (30–100 Hz) brain network activity plays an important role in the transmission of information between interconnected brain regions and between hemispheric cortices. Such oscillatory activity aggregates multisensory inputs in target regions for efficient spatial and temporal integration. Gamma-frequency oscillations have been shown to delay power decline in mouse models of Alzheimer's disease. Transcranial alternating current stimulation (ACS) projecting gamma waves has been shown to positively affect sustained enhancement of synaptic transmission in mouse models of Alzheimer's disease. Summary of the Invention [Means for solving the problem]

[0003] According to various embodiments of the present disclosure, novel methods are provided for improving and / or enhancing a user's cognitive abilities.

[0004] According to one embodiment of the present disclosure, there is provided a novel method for improving a user's cognitive performance, comprising: At least one session (S1, S2, . . . , S M displaying one or more visual training tasks to a user via a processor-implemented method step of displaying a visual training task to a user; Each session requires one or more responses from the user. s visual training tasks, A method is provided in which the visual training tasks are configured to induce an increase in brain frequency power based on a user's response to at least one visual training task displayed to the user.

[0005] In some embodiments, the method of the present disclosure comprises: K s In a given time step N (N = K, l ≦ K ≦ K s ) of session S1 including K time steps, receiving and / or collecting the user's response profile for the visual training task displayed at the given time step (N = K); analyzing the user's response profile received at a given time step (N = K) of session S1 and optionally any previous time step (N < K); repeating the above step of displaying a visual training task to the user at the next time step (K = K + 1) of session S1 until the number of time steps of session S1 reaches a predetermined number K s and / or it is determined that the response profile has reached a predetermined threshold.

[0006] In some embodiments, the method of the present disclosure K s In the last time step (N = K s ) of session S1 including K time steps (N = K, l ≦ K ≦ K s ), receiving and / or collecting the user's response profiles for the plurality of visual training tasks displayed at the time steps N (N = K, l ≦ K ≦ K s ) of session S1; analyzing the user's response profile received at the last time step (N = K s ) of session S1; repeating the above step of displaying a visual training task to the user at the next time step N (N = K, l ≦ K ≦ K s ) of the next session S2 until the number of time steps of session S1 reaches a predetermined number and / or it is determined that the response profile has reached a predetermined threshold.

[0007] In some embodiments, the induction of an increase in brain frequency power includes an increase in gamma wave power.

[0008] In some embodiments, inducing an increase in brain frequency power comprises: Increased P300 positive EEG component and and a reduction in P300 latency of the brain's response after the user responds to a visual training task displayed to the user.

[0009] In some embodiments, the visual training task is selected to induce an increase in brain frequency power based on the user's response to at least one visual training task and / or visual training stimulus image not previously displayed to the user by the method.

[0010] In some embodiments, the visual training task involves distinguishing between displays of target and non-target images.

[0011] In some embodiments, the increase in brain frequency power induced based on the response to the visual training task displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0012] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0013] According to another embodiment of the present disclosure, there is provided a method for improving brain coherence between visual and cognitive domains of a user, the method comprising: At least one session (S1, S2, . . . , S M displaying one or more visual training tasks to a user via a processor-implemented method step of displaying a visual training task to a user; Each session is sincluding individual visual training tasks, A method is provided, wherein the visual training task is configured to induce an increase in gamma wave power of the brain in a local area based on a user's response to at least one visual training task presented to the user.

[0014] In some embodiments, the method of the present disclosure K s at a given time step N (N = K, 1 ≤ K ≤ K s ) of a session S1 including K time steps, receiving and / or collecting a response profile of the user to the visual training task presented at the given time step (N = K); analyzing the response profile of the user received at a given time step (N = K) of session S1 and optionally any previous time step (N < K); repeating the above steps of presenting a visual training task to the user at the next time step (K = K + 1) of session S1 until the number of time steps of session S1 reaches a predetermined number of K ≤ K s and / or it is determined that the response profile has reached a predetermined threshold.

[0015] In some embodiments, the method of the present disclosure K s at the last time step (N = K s ) of session S1 including K time steps (N = K, 1 ≤ K ≤ K s ), receiving and / or collecting a response profile of the user to a plurality of visual training tasks presented at the time steps N (N = K, 1 ≤ K ≤ K s ) of session S1; analyzing the response profile of the user received at the last time step (N = K s ) of session S1; The next time step N (N=K, l≦K≦K) of the next session S2 is performed until a predetermined number of time steps of session S1 has been reached and / or the response profile is determined to have reached a predetermined threshold. s ) repeating the above step of displaying the visual training task to the user.

[0016] In some embodiments, the visual training task comprises: Increased P300 positive EEG component and and a reduction in P300 latency of a brain response after the user responds to a visual training task displayed to the user.

[0017] In some embodiments, the visual training task is selected to induce an increase in brain frequency power based on the user's response to at least one visual training task and / or visual training stimulus image not previously displayed to the user by the method.

[0018] In some embodiments, the increase in brain frequency power induced based on the response to the visual training task displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0019] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0020] According to another embodiment of the present disclosure, there is provided a method for improving a user's cognitive performance, comprising: At least one session (S1, S2, . . . , S M) to the user, Each session is s visual training stimulus images, A method is provided in which the visual training stimulus images are configured to induce an increase in brain frequency power based on a user's response to at least one visual training stimulus image displayed to the user.

[0021] In some embodiments, inducing an increase in brain frequency power comprises an increase in gamma wave power.

[0022] In some embodiments, inducing an increase in brain frequency power comprises: Increased P300 positive EEG component and and a reduction in the P300 latency of the brain response after the user responds to visual training stimulus images displayed to the user.

[0023] In some embodiments, the visual training stimulus images are selected to induce an increase in brain frequency power based on the user's response to at least one visual training stimulus image not previously displayed to the user by the method and / or the visual training task.

[0024] In some embodiments, the increase in brain frequency power induced based on the response to the visual training stimulus images displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0025] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0026] According to another embodiment of the present disclosure, there is provided a novel method for improving brain coherence between visual and cognitive domains of a user, the method comprising: At least one session (S1, S2, . . . , S M ) to the user, Each session is s visual training stimulus images, A method is provided in which the visual training stimulus images are configured to induce an increase in brain gamma wave power in a local region based on the user's response to at least one visual training stimulus image displayed to the user.

[0027] In some embodiments, the visual training stimulus image comprises: Increased P300 positive EEG component and and a shortening of the P300 latency of a brain response after the user responds to the visual training stimulus images displayed to the user.

[0028] In some embodiments, the visual training stimulus images are selected to induce an increase in brain frequency power based on the user's response to at least one visual training stimulus image not previously displayed to the user by the method and / or the visual training task.

[0029] In some embodiments, the increase in brain frequency power induced based on the response to the visual training stimulus images displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0030] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0031] According to another embodiment of the present disclosure, there is provided a novel method for improving a user's cognitive performance, comprising: At least one session (S1, S2, . . . , S M displaying one or more visual training tasks to a user via a processor-implemented method step of displaying a visual training task to a user; Each session requires one or more responses from the user. s visual training tasks, A method is provided in which visual training tasks are configured to induce long-term increases in brain frequency power based on a user's response to at least one visual training task displayed to the user, thereby improving the user's cognitive performance.

[0032] In some embodiments, the method of the present disclosure comprises: K s For a given time step N (N=K, l≦K≦K) of session S1, which contains s ) receiving and / or collecting a user's response profile to a visual training task presented at a given time step (N=K); Analyzing the response profile of the user received at a given time step (N = K) of session S1 and, optionally, any previous time step (N < K); The time step of session S1 is K ≤ K s Repeating the step of displaying a visual training task to the user at the next time step (K = K + 1) of session S1 until it is determined that the time step of session S1 has reached a predetermined number and / or the response profile has reached a predetermined threshold.

[0033] In some embodiments, the method of the present disclosure K s At the last time step (N = K, l ≤ K ≤ K s ) of session S1 including K s Receiving and / or collecting the response profile of the user for the plurality of visual training tasks displayed at the time step N (N = K, l ≤ K ≤ K s ) of session S1; Analyzing the response profile of the user received at the last time step (N = K s ) of session S1; Repeating the step of displaying a visual training task to the user at the next time step N (N = K, l ≤ K ≤ K s ) of the next session S2 until it is determined that the time step of session S1 has reached a predetermined number and / or the response profile has reached a predetermined threshold.

[0034] In some embodiments, the induction of an increase in brain frequency power includes an increase in gamma wave power.

[0035] In some embodiments, the induction of an increase in brain frequency power Includes an increase in the P300 positive brain wave component and At least one of a shortening of the P300 latency of the brain's response after the user has reacted to the visual training task presented to the user.

[0036] In some embodiments, the visual training task is selected to induce a long-term increase in brain frequency power based on the user's response to at least one visual training task and / or visual training stimulus image not previously displayed to the user by the method.

[0037] In some embodiments, the visual training task involves distinguishing between displays of target and non-target images.

[0038] In some embodiments, the long-term increase in brain frequency power induced based on the response to the visual training task displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0039] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0040] In some embodiments of the present disclosure, there is provided a method for improving brain coherence between visual and cognitive domains of a user, the method comprising: At least one session (S1, S2, . . . , S M displaying one or more visual training tasks to a user via a processor-implemented method step of displaying a visual training task to a user; Each session is s visual training tasks, A method is provided in which the visual training tasks are configured to induce a long-term increase in brain gamma wave power in a localized region based on the user's response to at least one visual training task displayed to the user.

[0041] In some embodiments, the method of the present disclosure K s For a given time step N (N = K, 1 ≤ K ≤ K s ) in session S1 including K time steps, receiving and / or collecting the user's response profile for the visual training task presented at the given time step (N = K); Analyzing the user's response profile received at a given time step (N = K) of session S1 and optionally any previous time step (N < K); Repeating the above steps of presenting a visual training task to the user at the next time step (K = K + 1) of session S1 until the number of time steps of session S1 reaches a predetermined number K s ≦ K and / or it is determined that the response profile has reached a predetermined threshold.

[0042] In some embodiments, the method of the present disclosure K s At the last time step (N = K s ) of session S1 including K time steps (N = K, 1 ≤ K ≤ K s ), receiving and / or collecting the user's response profile for the plurality of visual training tasks presented at time step N (N = K, 1 ≤ K ≤ K s ) of session S1; Analyzing the user's response profile received at the last time step (N = K s ) of session S1; Repeating the above steps of presenting a visual training task to the user at the next time step N (N = K, 1 ≤ K ≤ K s ) of the next session S2 until the number of time steps of session S1 reaches a predetermined number and / or it is determined that the response profile has reached a predetermined threshold.

[0043] In some embodiments, the visual training task is Increased P300 positive EEG component and and a reduction in P300 latency of a brain response after the user responds to a visual training task displayed to the user.

[0044] In some embodiments, the visual training task is selected to induce a long-term increase in brain frequency power based on the user's response to at least one visual training task and / or visual training stimulus image not previously displayed to the user by the method.

[0045] In some embodiments, the long-term increase in brain frequency power induced based on the response to the visual training task displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0046] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0047] According to another embodiment of the present disclosure, there is provided a novel method for inducing an increase in P300 positive electroencephalogram component and / or a decrease in P300 latency of a brain response after a user responds to a visual training task displayed to the user, the method comprising: At least one session (S1, S2, . . . , S M displaying one or more visual training tasks to a user via a processor-implemented method step of displaying a visual training task to a user; Each session is s visual training tasks, A visual training task is provided that is configured to induce, over a long period of time, an increase in the P300 positive brain wave component and / or a shortening of the P300 latency of the brain's response after the user has responded to at least one visual training task presented to the user, based on the user's response to the visual training task.

[0048] In some embodiments, the method of the present disclosure K s receiving and / or collecting a user's response profile for a visual training task presented at a given time step N (N = K, 1 ≤ K ≤ K s ) in a given time step N of session S1 that includes K analyzing the user's response profile received at a given time step N (N = K) of session S1 and optionally any previous time step (N < K); and repeating the above steps of presenting a visual training task to the user at the next time step (K = K + 1) of session S1 until the time steps of session S1 reach a predetermined number of K s and / or it is determined that the response profile has reached a predetermined threshold.

[0049] In some embodiments, the method of the present disclosure K s receiving and / or collecting the user's response profiles for a plurality of visual training tasks presented at time steps N (N = K, 1 ≤ K ≤ K s ) of session S1 at the last time step (N = K s ) of session S1 that includes K s time steps; analyzing the user's response profile received at the last time step (N = K s ) of session S1; and The next time step N (N=K, l≦K≦K) of the next session S2 is performed until a predetermined number of time steps of session S1 has been reached and / or the response profile is determined to have reached a predetermined threshold. s ) repeating the above step of displaying the visual training task to the user.

[0050] In some embodiments, the visual training task is further configured to induce an increase in gamma wave power.

[0051] In some embodiments, the visual training task is selected to induce a long-term increase in brain frequency power based on the user's response to at least one visual training task and / or visual training stimulus image not previously displayed to the user by the method.

[0052] In some embodiments, the long-term increase in brain frequency power induced based on the response to the visual training task displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0053] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0054] According to another embodiment of the present disclosure, there is provided a novel method for improving a user's cognitive performance, comprising: At least one session (S1, S2, . . . , S M ) to the user, Each session is s visual training stimulus images, A method is provided in which the visual training stimulus images are configured to induce a long-term increase in brain frequency power based on a user's response to at least one visual training stimulus image displayed to the user.

[0055] In some embodiments, inducing an increase in brain frequency power comprises an increase in gamma wave power.

[0056] In some embodiments, inducing an increase in brain frequency power comprises: Increased P300 positive EEG component and and a reduction in the P300 latency of the brain response after the user responds to visual training stimulus images displayed to the user.

[0057] In some embodiments, the visual training stimulus images are selected to induce a long-term increase in brain frequency power based on the user's response to at least one visual training stimulus image not previously displayed to the user by the method and / or the visual training task.

[0058] In some embodiments, the increase in brain frequency power induced based on the response to the visual training stimulus images displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0059] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0060] According to another embodiment of the present disclosure, there is provided a novel method for increasing gamma wave power in a user's brain, comprising: At least one session (S1, S2, . . . , S M ) to the user, Each session is s visual training stimulus images, A method is provided in which the visual training stimulus images are configured to induce a long-term increase in gamma wave power in the brain based on a user's response to at least one visual training stimulus image displayed to the user.

[0061] In some embodiments, the visual training stimulus image comprises: Increased P300 positive EEG component and and a shortening of the P300 latency of a brain response after the user responds to the visual training stimulus images displayed to the user.

[0062] In some embodiments, the visual training stimulus images are selected to induce a long-term increase in brain frequency power based on the user's response to at least one visual training stimulus image not previously displayed to the user by the method and / or the visual training task.

[0063] In some embodiments, the increase in brain frequency power induced based on the response to the visual training stimulus images displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0064] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0065] According to another embodiment of the present disclosure, there is provided a novel method for inducing an increase in P300 positive electroencephalogram component and / or a decrease in P300 latency of a brain response after a user responds to a visual training task displayed to the user, the method comprising: At least one session (S1, S2, . . . , S M ) to the user, Each session is s visual training stimulus images, A method is provided in which the visual training task is configured to induce an increase in the P300 positive electroencephalogram component and / or a decrease in the P300 latency of the brain response over an extended period of time after the user responds to the visual training task displayed to the user based on the user's response to at least one visual training stimulus image displayed to the user.

[0066] In some embodiments, the visual training stimulus images are further configured to induce an increase in gamma wave power.

[0067] In some embodiments, the visual training stimulus images are selected to induce a long-term increase in brain frequency power based on the user's response to at least one visual training stimulus image not previously displayed to the user by the method and / or the visual training task.

[0068] In some embodiments, the increase in brain frequency power induced based on the response to the visual training stimulus images displayed to the user is configured to improve a condition in a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0069] In some embodiments, the methods of the present disclosure further include use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

[0070] According to another embodiment of the present disclosure, there is provided a novel system configured to display an image to a user, the system comprising: at least one processor configured to perform the method steps; and at least one display device configured to display visual training tasks and / or visual training stimulus images to a user.

[0071] In some embodiments, the system of the present disclosure further comprises at least one input device configured to collect and analyze a user's response to images displayed to the user at any given time step (N=K).

[0072] In some embodiments, the system of the present disclosure further comprises a device selected from a computer, a smartphone, a tablet, and any combination thereof.

[0073] In some embodiments, the system of the present disclosure further comprises at least one of a data storage for storing input from a user and analysis of the input, an input device, a speaker device, a microphone device, and a computer mouse. [Brief explanation of the drawings]

[0074] The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of this specification. However, the invention, both as to organization and method of operation, together with its objects, features, and advantages, may best be understood by reading the following detailed description when read in conjunction with the accompanying drawings.

[0075] [Figure 1A] FIG. 1A is a diagram that schematically illustrates an example of a visual training task according to some embodiments of the present disclosure. [Figure 1B] FIG. 1B is a diagram that schematically illustrates an example of a visual training task according to some embodiments of the present disclosure. [Figure 1C] FIG. 1C is a diagram that schematically illustrates an example of a visual training task according to some embodiments of the present disclosure. [Figure 2A] FIG. 2A is a diagram that schematically illustrates various examples of visual training tasks according to some embodiments of the present disclosure. [Figure 2B] FIG. 2B is a diagram that schematically illustrates various examples of visual training tasks according to some embodiments of the present disclosure. [Figure 2C] FIG. 2C is a diagram that schematically illustrates various examples of visual training tasks according to some embodiments of the present disclosure. [Figure 2D] FIG. 2D is a diagram that schematically illustrates various examples of visual training tasks according to some embodiments of the present disclosure. [Figure 2E] FIG. 2E is a diagram that schematically illustrates various examples of visual training tasks according to some embodiments of the present disclosure. [Figure 2F] FIG. 2F is a diagram that schematically illustrates various examples of visual training tasks according to some embodiments of the present disclosure. [Figure 2G] FIG. 2G is a diagram that schematically illustrates various examples of visual training tasks according to some embodiments of the present disclosure. [Figure 3] FIG. 3 is a diagram that schematically illustrates an apparatus and method steps for displaying a visual training task according to some embodiments of the present disclosure. [Figure 4]FIG. 4 is a diagram that schematically illustrates an apparatus and method steps for displaying a visual training task according to some embodiments of the present disclosure. [Figure 5] 5A and 5B are diagrams that schematically illustrate example visual training stimulus images, according to some embodiments of the present disclosure. [Figure 6] 6A-C are diagrams that schematically illustrate examples of visual training tasks and their EEG measurements. [Figure 7] 7A-L are diagrams that schematically illustrate example EEG measurements after the visual training stimulus images of FIGS. 5A and B are displayed to a user. [Figure 8] 8A-D are diagrams showing four examples of visual training stimulus images and their EEG measurement values. [Figure 9] 9A-D are diagrams that schematically illustrate an example of a visual training task (FIG. 9D) and pre- and post-training EEG measurements using it. [Figure 10] 10A-D are diagrams illustrating an example of a visual training stimulus image (FIG. 10D) and its EEG measurements before and after a session displaying the visual training task of FIG. 9D. [Figure 11] FIG. 11 is a diagram showing a schematic diagram of EEG sensor mapping. [Figure 12] FIG. 12 is a diagram that schematically illustrates an example of a visual training task that includes a working memory (WM) task, according to some embodiments of the present disclosure. [Figure 13A] FIG. 13A shows the results (accuracy and reaction time) of 12 subjects when they were presented with a visual training task containing one display element, two display elements, or three display elements. [Figure 13B] FIG. 13B shows the results (accuracy and reaction time) of 12 subjects when they were presented with a visual training task containing one display element, two display elements, or three display elements. [Figure 14A] FIG. 14A shows gamma coherence for the same visual training task and subject as in FIGS. 13A and 13B. [Figure 14B]FIG. 14B shows gamma coherence for the same visual training task and subject as in FIGS. 13A and 13B. [Figure 15A] 15A to 15D are diagrams showing gamma coherence before training and before and after training for two subjects. [Figure 15B] 15A to 15D are diagrams showing gamma coherence before training and before and after training for two subjects. [Figure 15C] 15A to 15D are diagrams showing gamma coherence before training and before and after training for two subjects. [Figure 15D] 15A to 15D are diagrams showing gamma coherence before training and before and after training for two subjects. [Figure 15E] 15E-15H show the results of cognitive improvement in the two subjects in FIGS. 15A-15D. [Figure 15F] 15E-15H show the results of cognitive improvement in the two subjects in FIGS. 15A-15D. [Figure 15G] 15E-15H show the results of cognitive improvement in the two subjects in FIGS. 15A-15D. [Figure 15H] 15E-15H show the results of cognitive improvement in the two subjects in FIGS. 15A-15D. [Figure 16A] 16A to 16D are diagrams showing the differences in P300 responses (target images vs. non-target images), P300 amplitude, and P300 latency during a working memory (WM) task before and after training for a subject. [Figure 16B] 16A to 16D are diagrams showing the differences in P300 responses (target images vs. non-target images), P300 amplitude, and P300 latency during a working memory (WM) task before and after training for a subject. [Figure 16C] 16A to 16D are diagrams showing the differences in P300 responses (target images vs. non-target images), P300 amplitude, and P300 latency during a working memory (WM) task before and after training for a subject. [Figure 16D] 16A to 16D are diagrams showing the differences in P300 responses (target images vs. non-target images), P300 amplitude, and P300 latency during a working memory (WM) task before and after training for a subject. [Figure 16E] 16E and 16F are diagrams showing the relationship between frequency power response and blur level for both P7+P8 and Fz. [Figure 16F] 16E and 16F are diagrams showing the relationship between frequency power response and blur level for both P7+P8 and Fz. [Figure 17] FIG. 17 illustrates a "Count Dogs" task, according to some embodiments of the present disclosure. [Figure 18A] 18A and 18B show the P300 response measured during the "counting dogs" task shown in FIG. [Figure 18B] 18A and 18B show the P300 response measured during the "counting dogs" task shown in FIG.

[0076] It will be understood that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or similar elements. DETAILED DESCRIPTION OF THE INVENTION

[0077] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present invention.

[0078] The entire disclosure of PCT Patent Application No. PCT / IL22 / 51147, published as WO2023 / 073715A2, is incorporated herein by reference.

[0079] In some embodiments, cognitive decline is associated with impaired (decreased) brain oscillatory activity. Brain oscillatory activity underlies our brain function and determines how we think and respond to the world around us. Synchronized activity of nerve cells (neurons) generates brain oscillations (rhythms), which transmit information to various parts of the brain and coordinate responses to internal and external stimuli. Over the past few decades, extensive research has been conducted examining changes in brain oscillations (rhythms) in age-related cognitive decline and Alzheimer's disease (AD). Recent evidence generally supports the validity of resting-state electroencephalography / magnetoencephalography (rsEEG / MEG) as a noninvasive predictive biomarker for neurodegenerative diseases such as mild cognitive impairment (MCI) and Alzheimer's disease (AD).

[0080] In some embodiments, electroencephalography (EEG) is a method of recording electrical activity on the scalp and has been shown to represent macroscopic activity in the superficial layers of the brain below the scalp. Electroencephalography (EEG) is typically non-invasive, with electrodes placed along the scalp. Electroencephalography (EEG) measures fluctuations in voltage (V) due to ionic current (I) within the brain's neurons. Clinically, electroencephalography (EEG) refers to the recording of spontaneous brain electrical activity over a period of time using multiple electrodes placed on the scalp.

[0081] In some embodiments, electroencephalography (EEG) was performed using 30 dry electrodes (no gel) and a Cognionics® wireless headset at a sampling rate of 500 Hz during the visual training task and / or presentation of visual training stimulus images, as described below. Time-frequency power analysis was performed in the gamma frequency band (30-50 Hz) over a specific time window (approximately 0.5 seconds).

[0082] In some embodiments, diagnostic applications generally focus on either event-related potentials (ERPs), which examine potential fluctuations time-synchronized to events such as "stimulus onset" or "button press," or on the spectral content of EEG, which analyzes the types of neural oscillations (commonly referred to as "brain waves") observed in the frequency domain of EEG signals.

[0083] In some embodiments, the electrical charge of the brain is maintained by billions of neurons. Scalp EEG activity shows oscillations of various frequencies. Some of these oscillations have characteristic frequency ranges, i.e., spatial distributions, and are associated with various states of brain function. These oscillations represent synchronized activity on networks of neurons.

[0084] In some embodiments, the P300, a biomarker of cognitive performance, reflects the speed of neuronal processing of basic stimuli that require cognitive judgment.

[0085] In some embodiments, the P300 is measured using EEG as a positive electroencephalographic component in brain oscillatory activity measured over approximately 300 milliseconds after stimulus onset (P300a). It distinguishes between target stimuli, which are primarily influenced by attention, and non-target stimuli, which are primarily influenced by slower cognitive processes (approximately 400-500 milliseconds), such as short-term memory or decision-making (P300b). The P300 is believed to be generated in the hippocampus, amygdala, thalamus, and basal ganglia. The P300 can be used as a tool to measure cognitive impairment in various neurological disorders, particularly those related to attention and short-term memory. The P300 reflects early cognitive changes in mild cognitive impairment (MCI) and predicts dementia in elderly individuals with Alzheimer's disease.

[0086] In some embodiments, the biomarker is the power of gamma oscillatory activity (20-80 Hz). In some embodiments, calculating gamma power requires comparing a measured value (e.g., a memory task) to a baseline. Thus, this is 10 log (memory case / baseline). In some embodiments, gamma oscillatory activity contributes to a wide range of human cognitive abilities, such as attention, perception, object recognition, memory processes, face recognition, and emotion paradigms, suggesting that gamma synchronization is a fundamental process for various brain functions. A decrease in gamma power is observed with age, i.e., normal healthy aging, in the occipito-parietal and frontal cortices. In MCI and AD groups, decreased resting gamma power / synchronization and delayed gamma responses, as well as increased gamma-band power or connectivity (i.e., cross-frequency coupling (CFC)), are observed compared to normal healthy aging.

[0087] Some embodiments of the present disclosure provide novel training methods using visual training tasks and / or stimuli configured to induce sustained, stimulus-independent changes in neural oscillations in a subject's brain. The methods provided by the present disclosure are novel training methods using visual training tasks and / or stimuli configured to induce and / or induce long-term changes in brain oscillations that are not limited by the type of stimulus. The changes in brain oscillations modulate neural activity, resulting in sustained changes that generalize to untrained functions or stimuli.

[0088] In particular, the novel training method disclosed herein is configured to increase gamma oscillation power in response to visual training tasks and / or stimuli presented to a user (subject). After completion of the training, the user's gamma oscillation power has been demonstrated to increase compared to pre-training baseline values. The novel training method disclosed herein also results in an increase in gamma oscillation power in response to various types of visual training tasks and / or stimuli not included in the training process (untrained tasks and / or stimuli).

[0089] In some embodiments, novel training methods using visual training tasks and / or stimuli according to the present disclosure are configured to induce changes in brain waveforms that result in improvements in P300 (increased amplitude and / or decreased latency) compared to pre-training baseline values, not only for the visual training tasks and / or stimuli used in the training, but also for visual training tasks and / or stimuli not used in the training.

[0090] In some embodiments, amplitude (μV) is defined as the difference between the mean pre-stimulus baseline voltage and the maximum positive-going peak of the event-related potential (ERP) waveform within a time window (e.g., 250-500 ms, although this range varies depending on the stimulation modality, task conditions, subject age, etc.). Latency (milliseconds) is typically defined as the time from stimulus onset to the maximum positive-going amplitude within the time window. The neuropsychological origins of the P300a and P300b subcomponents are as follows: P300a and P300b are generated by distinct neural sites. Cognitive models suggest that P300a originates from stimulus-driven frontal lobe attention mechanisms during task processing, whereas P300b originates from attention-related temporoparietal activity and is associated with subsequent memory processing.

[0091] In some embodiments, as used herein, the terms "electroencephalogram" and "neural oscillation" refer to electrical potentials or impulses emitted by brain tissue. In some embodiments, as used herein, the term "frequency power" refers to the measured electrical power of neural oscillations. In some embodiments, frequency power measurements are obtained by waveform convolution (Fast Fourier Transform) and are typically expressed in decibels (dB).

[0092] In some embodiments of the present disclosure, the term "amplitude" as used herein refers to the difference between the maximum value of the P300b and its minimum value before training, as shown in Figures 9A and 9C.

[0093] In some embodiments of the present disclosure, the term "latency" as used herein more specifically refers to the "latency of P300b," which is the time difference between the maximum value of P300b and the minimum value before training, as shown in FIG. 9A.

[0094] In some embodiments of the present disclosure, the term "response" as used herein refers to a user's active response (answer) to a requested task.

[0095] In some embodiments of the present disclosure, the term "reaction" as used herein refers to a user's electroencephalographic measurements and / or brain-evoked activity in response to a requested visual training task and / or displayed visual training stimuli.

[0096] According to one embodiment of the present disclosure, there is provided a novel method for improving and / or enhancing a user's cognitive performance, comprising: At least one session (S1, S2, . . . , S M displaying one or more visual training tasks to a user via a processor-implemented method step of displaying a visual training task to a user; Each session requires one or more responses from the user. s visual training tasks, A method is provided in which the visual training tasks are configured to induce an increase in brain frequency power based on a user's response to at least one visual training task displayed to the user.

[0097] According to another embodiment of the present disclosure, there is provided a method for improving a user's cognitive performance, comprising: At least one session (S1, S2, . . . , S M ) to the user, Each session is s visual training stimulus images, A method is provided in which the visual training stimulus images are configured to induce an increase in brain frequency power based on a user's response to at least one visual training stimulus image displayed to the user.

[0098] According to another embodiment of the present disclosure, there is provided a method for improving brain coherence between visual and cognitive domains of a user, the method comprising: At least one session (S1, S2, . . . , S M displaying one or more visual training tasks to a user via a processor-implemented method step of displaying a visual training task to a user; Each session is s visual training tasks, A method is provided in which the visual training tasks are configured to induce an increase in brain gamma wave power in a local region based on a user's response to at least one visual training task displayed to the user.

[0099] According to another embodiment of the present disclosure, there is provided a novel method for improving brain coherence between visual and cognitive domains of a user, the method comprising: At least one session (S1, S2, . . . , S M ) to the user, Each session is s visual training stimulus images, A method is provided in which the visual training stimulus images are configured to induce an increase in brain gamma wave power in a local region based on the user's response to at least one visual training stimulus image displayed to the user.

[0100] According to some embodiments of the present disclosure, novel methods 300, 400 are provided for displaying one or more visual training tasks to a user (subject), as shown, for example, in Figures 1A-1C, 2A-2G, 3, and 4. The methods of the present disclosure include a K-mode that requests one or more responses from the user. s The method includes processor-implemented steps 310, 410 of displaying at least one session including the visual training task to the user.

[0101] In some embodiments, the visual training tasks are selected or configured such that the required response provides long-term improvement in the user's response through better cognitive and / or motor responses to at least the displayed visual training task, and optionally to other visual training tasks.

[0102] According to some embodiments of the present disclosure, novel methods 300, 400 are provided for displaying one or more visual training tasks to a user, as shown, for example, in Figures 1A-1C, 2A-2G, 3, and 4. The methods of the present disclosure include a K-mode that requests one or more responses from the user. s The method includes processor-implemented method steps 310, 410 of displaying to a user at least one session including a visual training task, the visual training task selected such that a required response induces and / or provokes a long-term increase in brain frequency power in response to at least the displayed visual training task, and optionally to other visual training tasks.

[0103] In some embodiments of the present disclosure, sessions including the above visual training tasks are configured to train the user to improve their responses and / or brain reactions.

[0104] In some embodiments, visual training task 110 includes at least one image 115, as shown in FIG. 1A. In some embodiments, visual training task 130 includes at least two images 135, 136, as shown in FIG. 1B. In some embodiments, visual training task 150 includes at least two images 155, 156, as shown in FIG. 1C. In some embodiments, visual training task 200 includes multiple visual training tasks and / or visual training stimulus images 220-270, as shown in FIGS. 2A-2G.

[0105] In some embodiments, the disclosed method includes communicating instructions to the user regarding the displayed visual training task. In some embodiments, the instructions are general and the same for all sessions and all time steps. In some embodiments, the instructions vary from session to session. In some embodiments, the instructions vary from time step to time step. In some embodiments, the instructions are communicated verbally from a training personnel to the user. In some embodiments, the instructions are communicated to the user via a processor and at least one device selected from a display device and a speaker.

[0106] In some embodiments, the visual training task includes at least one instruction to the user that requires a response from the user, as shown, for example, at 120 in FIG. 1A, at 140 in FIG. 1B, at 160 in FIG. 1C, and at 210 in FIG. 2A.

[0107] In some embodiments, the user's response includes at least one of the following: Cognitive responses; non-limiting examples include answering questions (e.g., yes / no, true / false, counting the number of target display elements), selecting display elements based on criteria, etc. Motor responses; non-limiting examples include eye / limb movements (e.g., tracking a display element with a cursor, capturing a display element with a cursor), etc. · Emotional responses (e.g., How do you feel?). · Behavioral response (e.g., what will you do?). · Reflexive response (e.g., tracking a display element with a cursor, capturing a display element with a cursor). A reflexive response may include both a cognitive response and a motor response. · Eye response (e.g., eye movement, blink, pupil dynamics).

[0108] In some embodiments, the term "long period" refers to a change in brain waves that persists for over an hour and extends over several months.

[0109] In some embodiments, as shown in, for example, FIG. 3, the method 300 of the present disclosure K s In a given time step N (N = K, l ≦ K ≦ K s ) of a session S1 including K time steps, receiving and / or collecting a response profile of a user for a visual training task displayed at the given time step (N = K) in step 320; Analyzing, in step 330, the response profile of the user received at a given time step (N = K) of session S1 and optionally any previous time step (N < K); Repeating, in step 340, the above step of displaying a visual training task to the user in the next time step (K = K + 1) of session S1 until the number of time steps of session S1 reaches a predetermined number of K ≦ K s , and / or it is determined that the response profile has reached a predetermined threshold. Further comprising.

[0110] In some embodiments, as shown in, for example, FIG. 4, the method 400 of the present disclosure K s In the last time step (N = K s ) of session S1 including K time steps (N = K, l ≦ K ≦ K s ), receiving and / or collecting, in step 420, a response profile of the user for a plurality of visual training tasks displayed at time step N (N = K, l ≦ K ≦ K s ) of session S1; The last time step of session S1 (N=K s ) a step 430 of analyzing the user response profile received; The next time step N (N=K, l≦K≦K) of the next session S2 is performed until a predetermined number of time steps of session S1 has been reached and / or the response profile is determined to have reached a predetermined threshold. s ) repeating the above steps of presenting the visual training task to the user.

[0111] Reference is now made to Figures 2A-2G. Figure 2A shows instructions to a user to count the number of display elements in the form of vertical lines 204. The display elements in the form of vertical lines 204 for correct responses are indicated by reference numeral 202, and the display elements in the form of slanted lines for incorrect responses are indicated by reference numeral 203. The visual training tasks 220-270 of Figures 2B-2G can be used in both methods 300 and 400 of the present disclosure. For example, according to steps 320 and 330 of method 300 of the present disclosure, six responses are collected and analyzed after the display of each task / image. Alternatively, according to steps 420 and 430 of method 400 of the present disclosure, the user is requested to count the number of display elements in the form of vertical lines 202 from all visual training tasks / visual training stimulus images after the display of all visual training tasks / visual training stimulus images 220-270, and a single response is collected and analyzed.

[0112] In some embodiments, the response profile includes at least one of the following: · Correctness of the answer (e.g. True / False). · Quality of answers (e.g., percentage of correct answers). In some embodiments, the percentage of correct answers is examined for each task type. Response time (i.e., response time for each task). In some embodiments, the time profile for each type of task is analyzed.

[0113] In some embodiments, the methods of the present disclosure further include measuring the user's brain signals using an EEG device and analyzing the measurements. In some embodiments, the EEG measurements and analysis are performed after training. In some embodiments, the EEG measurements and analysis are performed during training. In some embodiments, the EEG measurements and analysis are performed before training. In some embodiments, the EEG measurements and analysis are performed before, during, and / or after training.

[0114] In some embodiments, EEG measurements and analysis are used to select visual training tasks and / or images in at least some of the methods of the present disclosure according to training and / or treatment goals. In such embodiments, visual training tasks and / or images are selected by analyzing brain responses to their presentation during and / or over an extended period of time after their presentation. In some embodiments, visual training tasks and / or images are selected by comparison with measurements taken before their presentation.

[0115] In some embodiments, EEG measurements and analysis are performed for each particular user and used to select visual training tasks and / or images for training and / or treating the particular user according to the training and / or treatment goals.

[0116] In some embodiments, EEG measurements and analysis are performed on multiple users and used to select visual training tasks and / or images for training and / or treatment of those users according to training and / or treatment goals.

[0117] In some embodiments, the EEG measurements analyzed are taken by sensor channels selected from Fz, P7, P8, O1, O2, and any combination thereof (see FIG. 11 ). In some embodiments, the measurements of channel Fz are analyzed to assess cognitive processing. In some embodiments, the measurements of channels P7 and / or P8 are analyzed to assess visual working memory processing. In some embodiments, the measurements of channels O1 and / or O2 are analyzed to assess visual processing.

[0118] In some embodiments, the EEG measurements measured and / or analyzed include frequency power. In some embodiments, long-term increases in brain frequency power induced in response to displayed visual training tasks (e.g., tasks displayed in a training session or other tasks) and / or images are measured and / or analyzed by EEG. In some embodiments, selection of visual training tasks and / or images for inducing long-term increases in brain frequency power is based on analysis of EEG measurements.

[0119] In some embodiments, the EEG measurements measured and / or analyzed include gamma wave power, and in some embodiments, the selection of visual training tasks and / or images to induce long-term increases in brain gamma wave power (global and / or localized brain regions) is based on the analysis of the EEG measurements.

[0120] In some embodiments, the EEG measurements measured and / or analyzed include a P300 positive electroencephalogram component, and in some embodiments, the selection of visual training tasks and / or images to increase the P300 positive electroencephalogram component is based on an analysis of the EEG measurements.

[0121] In some embodiments, the EEG measurements measured and / or analyzed include P300 latency of the brain response, and in some embodiments, the selection of visual training tasks and / or images to decrease P300 latency of the brain response is based on the analysis of the EEG measurements.

[0122] In some embodiments, EEG measurements measured and / or analyzed include coherence, which refers to synchronization between brain regions. In some embodiments, coherence is measured between visual processing regions and cognitive processing regions, as the level of coherence is involved in cognitive processes that require coordination of long-range brain networks, as shown for gamma waves in Figure 2 of "Changes of Functional and Directed Resting-State Connectivity Are Associated with Neuronal Oscillations, ApoE Genotype, and Amyloid Deposition in Mild Cognitive Impairment, Michels et al., Frontiers in Aging Neuroscience (2017)."

[0123] In some embodiments, EEG coherence between two electrodes measures the similarity or synchronization of electrical activity at specific locations in the brain where the two electrodes are placed. To ensure accuracy of EEG coherence estimates, several data preprocessing steps can be performed.

[0124] In a non-limiting example of coherence calculation, noise and artifacts (including eye blinks) are first removed by applying a 180 μV cutoff filter to the global field mean potential and an additional 70 μV filter to the frontal electrode mean potential. The data is then filtered to extract the frequency band of interest (e.g., the gamma frequency band: 30-50 Hz). Next, the cross-spectral density (CSD) between signals from two separate scalp sites (i.e., the Fz electrode in the frontal lobe and electrodes P7 and P8 in the parietal lobe on both sides of the cortex) is calculated. This determines the phase relationship between the two signals. Finally, the coherence value is calculated by dividing the magnitude of the CSD by the square root of the product of the power spectra of the two signals. This normalization determines the overall power level difference between the two signals. The coherence between the x and y waveforms was calculated spectrally using the following formula:

[0125]

number

[0126] In the above formula, G xy (f) is the average cross power spectral density, and G xx (f) and G yy (f) is the average autopower spectral density of each (Jiang, Z. yan. Study on EEG power and coherence in patients with mild cognitive impairment during working memory task. J. Zhejiang Univ. Sci. B. 6, 1213-1219; 2005).

[0127] In some embodiments, the response profile comprises electroencephalography (EEG) and / or analysis of measurements thereof according to any of the methods described above.

[0128] In some embodiments, the predetermined threshold is at least one selected from the following: - A predetermined number of sessions. · A predetermined level of response accuracy, quality, and / or time. Saturation of measured levels of brain response.

[0129] In some embodiments, the visual training task includes at least one of an attention demanding, a perception task, an object recognition task, a memory task, a face recognition task, and an emotional incentive task.

[0130] In some embodiments, the visual training task involves distinguishing between a display of a target image 136 and a display of a non-target image 135, as shown, for example, in FIG. 1B.

[0131] In some embodiments, inducing an increase in brain frequency power comprises at least one of the following: Increased electrical peak (e.g., P300b, denoted as P3b in Figures 9A and 9C) A shortening of the latency (L) of brain responses after the presentation of the visual training task (e.g., P300b, denoted as P3b in Figure 9A, L post <L pre ).

[0132] In some embodiments, the visual training task (e.g., a task for recognizing target images, FIG. 9D ) is selected or configured to induce a long-term increase in brain frequency power in response to at least one new visual training task (e.g., a passive task displaying synchronized motion images, FIG. 10D ) that was not presented to the user during the training session. Thus, the visual training task ( FIG. 9D ) is configured to induce a long-term increase in brain frequency power in response to the new visual training task ( FIG. 10D ) and / or new visual training stimulus images.

[0133] In one embodiment of the present disclosure, a novel method is provided for displaying one or more visual stimuli (visual training stimulus images) to a user. The visual stimuli include passive stimuli that do not require the user to perform a task. The method of the present disclosure includes: s The method includes displaying to the user at least one session including a set of visual training stimulus images selected to induce and / or elicit long-term increases in brain frequency power in response to at least the displayed visual training stimulus images, and optionally other visual training stimulus images, examples of which are shown in Figures 5A, 5B, 8A (labeled 801), 8B (labeled 802), 8C (labeled 803), and 8D (labeled 804).

[0134] In some embodiments, the visual stimulus (visual training stimulus image) includes at least one dynamic display element. Dynamic means that the display element moves during image display. Figures 5A and 5B illustrate visual stimuli including multiple dynamic display elements (the direction of movement of the dynamic display elements is indicated by arrows). Figure 5A illustrates a synchronized stimulus image 510 in which all dynamic display elements 515 move in the same direction and at the same speed. Figure 5B illustrates an asynchronous stimulus image 520 in which at least one dynamic display element 526 moves in a different direction and at a different speed than at least one other dynamic display element 525.

[0135] In some embodiments, the long-term increase in brain frequency power induced in response to the displayed visual training task and / or visual training stimulus images improves the condition of a patient suffering from at least one of poor cognitive response, poor motor response, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, and any combination thereof.

[0136] In some embodiments, the present disclosure provides a system 390 configured to display an image to a user. at least one processor 391 configured to perform the method steps according to any of the above method steps; At least one display device 392 configured to display visual training tasks and / or visual training stimulus images to the user according to any of the above embodiments.

[0137] In some embodiments, the system of the present disclosure further comprises at least one input device 393 configured to input a user's response to the displayed image at any given time step (N=K).

[0138] In some embodiments, the system of the present disclosure further comprises a device selected from a computer, a smartphone, a tablet, and any combination thereof.

[0139] In some embodiments, the system of the present disclosure further comprises at least one of a data storage device 394 for storing input from a user and analysis of the input, an input device, a speaker device, a microphone device, and a computer mouse.

[0140] Experiments and Results

[0141] EEG measurement and analysis for at least some of the above methods will now be described with reference to Figures 6A-10D.

[0142] Figure 6A shows EEG P300 measurements (voltage versus time) from EEG sensor Pz (Figure 6B), which is associated with the prefrontal cortex. This measurement was taken during a task requiring the user to identify images containing vertical elements, as shown in Figure 6C (the upper image contains a vertical element, and the lower image contains a tilted element). The graph lines in Figure 6A represent brain responses. The solid graph line represents the response when the user is presented with an image containing a display element (vertical element) in the form of a vertical line (0°) (25% of the display in this test). The dotted graph line represents the response when the user is presented with an image containing a display element (tilted element) in the form of a tilted line (75% of the display in this test). This example demonstrates that the easy decision to recognize a vertical element provides a stronger stimulus to the brain than the difficult decision to recognize a tilted element. Thus, the EEG responses to images containing vertical elements ("targets") are significantly different from those to images containing tilted elements, serving as a measure (marker) of cognitive ability to pay specific attention to the visual training task.

[0143] 7A-7H show EEG analysis frequency versus time versus colored amplitude (low = green, high = red) after displaying the visual training stimulus images 510, 520 to a user as shown in FIGS. 5A and 5B. Each panel shows measurements from a different electrode. FIGS. 7A and 7E show measurements from the EEG frontal lobe Fz sensor shown in FIG. 7I. FIGS. 7B and 7F show measurements from the EEG temporal lobe P8 sensor shown in FIG. 7J. FIGS. 7C and 7G show measurements from the EEG MT / V5 PO8 sensor shown in FIG. 7K. FIGS. 7D and 7H show measurements from the EEG occipital lobe POZ sensor shown in FIG. 7L. FIGS. 7A-7D show the brain response of a user displayed a synchronized stimulus image 510 with a vertical element 515 moving synchronously (from left to right). 7E-7H show the brain response of a user presented with an asynchronous stimulus image 520 in which vertical elements 515, 516 move asynchronously (left or right).

[0144] Therefore, it can be seen that the synchronized image 510 (FIGS. 7A to 7D) has more gamma band (red / dark areas) than the non-synchronized image 520 (FIGS. 7E to 7H). In other words, it can be seen that the non-synchronized image is less stimulating to the brain than the synchronized image.

[0145] 8A-8D show EEG-analyzed P300 measurements of frequency versus time versus colored amplitude (low = green, high = red) during and after the presentation of four visual training stimulus images 801-804 to a user. FIG. 8C shows that visual training stimulus image 803 induces greater gamma frequencies than the other visual training stimulus images 801, 802, and 804. Visual training stimulus image 803 induces gamma frequencies above 40 Hz (T axis), resulting in higher activity (i.e., more areas becoming red).

[0146] Figures 9A-9D show the visual training task and its EEG measurements. A 68-year-old healthy user without cognitive impairment underwent 90 15-minute training sessions (per day). The user was asked to distinguish between images containing vertical elements and images containing tilted elements, as shown in Figure 9D. Figure 9A shows the P300 measurements before (dotted line) and after (plain line) training, as measured by the EEG frontal Fz sensor (Figure 9B). Figure 9C shows an increase in the amplitude between the P3b peak and its preceding minimum before and after training. Figure 9A shows that the latency after training was shorter than that before training (L post <L pre ), showing that the amplitude of the post-training P300a reaches its peak level earlier (approximately 15 ms) than the pre-training P300a, and then continues to increase in amplitude.

[0147] Figures 10A-10D show examples of visual training stimulus images (Figure 10D) and their corresponding EEG frequency measurements for a 68-year-old healthy user before and after the visual training task shown in Figure 9D. After 90 training sessions (each 15 minutes, per day), the user was presented with new, unseen visual training stimulus images (Figure 10D). Figure 10A shows pre-training (upper panel) and post-training (lower panel) frequency measurements measured by the EEG POz sensor (Figure 10B). Figure 10C shows the gamma frequency integral in the 0.7 ms and 1.2 ms time windows (circled to indicate the range of analysis), demonstrating the change (increase) between pre-training and post-training. The gamma band in this time window increased after training, as shown in the bar graph (Figure 10C).

[0148] FIG. 12 illustrates an example of a visual training task including a working memory (WM) task, according to some embodiments of the present disclosure. In this visual training task, at each time step, an image containing one, two, or three colored display elements is displayed (four arbitrary display elements × six arbitrary colors). FIG. 12 also illustrates an example timeline for the visual training task, in which only one display element is displayed in a set of two image displays (i.e., each image contains one display element). In this example, the user is asked to remember and report whether a display element of a particular shape (a "target") of a particular color is displayed. For example, in this example, after a set of two image displays, a red triangular display element is displayed. In the timeline illustrated in FIG. 12, An image containing a red circular display element (non-target image) is displayed for 0.5 seconds. An image containing a crosshair-shaped display element is then displayed for 1.5 seconds. Afterwards, an image containing a red triangular display element (the target image) is displayed for 0.5 seconds. An image containing a crosshair-shaped display element is then displayed for 2.5 seconds. The only question asked of the user / subject is whether the target image appeared or not.

[0149] A similar experiment was conducted on 12 subjects (n = 12, 6 young subjects under 40 years of age and 6 elderly subjects over 60 years of age). Subjects were instructed to memorize the shape and color of display elements contained in a target image and then report whether or not they matched the correct answer. Target and non-target images (test images) were displayed for 0.5 seconds. The shape and / or color of display elements contained in the non-target images (test images) were randomly changed. EEG measurements were performed during the visual training task using 30 dry electrodes (without gel) and a Cognionics® wireless headset at a sampling rate of 500 Hz. Time-frequency power analysis was performed in the gamma frequency band (30–50 Hz) over a specific time window (approximately 0.5 seconds). EEG coherence was calculated as the frequency-normalized cross-gamma power spectrum of signals recorded at the frontal and parietal lobes of the scalp. Results before training are shown in Figures 13 and 14. The results before and after the training are shown in FIGS.

[0150] Figures 13A and 13B show the results of a memory test in which 12 subjects (n = 12, 6 younger than 40 years old and 6 older than 60 years old) were presented with a visual training task containing one, two, or three display elements (colored elements), as shown in Figure 12. Figure 13A shows accuracy (ranging from 0 to 1), and Figure 13B shows response time (milliseconds). As expected, the younger subjects performed better in the memory test and had better response times than the older subjects.

[0151] Figures 14A and 14B show gamma coherence in the visual training task and subjects whose results are shown in Figures 13A and 13B. Gamma coherence, analyzed using EEG measurements, was calculated between frontal and parietal sites (Figure 14A) and between two parietal sites across hemispheres (Figure 14B). · The working memory (WM) load effect is the partial effect of the number of display elements contained in an image on coherence. · Age effects are partial effects of age groups. Linear mixed-effects (LME) models are particularly useful when dealing with repeated measures or nested data. Unlike two-way ANOVA with two independent variables, they do not assume equal variance between groups. This allows for a more realistic representation of the underlying structure in the data and allows for the estimation of both fixed and random effects. It is an extension of the linear regression model, which allows for the incorporation of both fixed and random effects. Fixed effects represent population-level effects. They are similar to the coefficients in a standard linear regression model. They capture the average relationship between the independent and dependent variables across all levels. Random effects account for variability at different levels of the hierarchy. Unlike fixed effects, random effects are considered to be drawn from a larger population and are used to model variability across different groups or clusters. Random effects are introduced to account for correlation and heterogeneity within groups. The results shown in Figures 14A and 14B show that memory load significantly increased (improved) coherence, and that coherence in younger subjects (age < 40) was significantly higher than that in older subjects (age > 60). Significance was assessed using a linear mixed-effects model (LME).

[0152] Figures 15A-15H and 16A-16F relate to the following experiment. The experiment used a blurred visual training task, also described in International Publication WO 2023 / 073715A2. The training consisted of a set of 10-15 minute training sessions. Each training session included a series of visual training tasks. In the visual training task, an 80-year-old subject (subject number 1) and a 67-year-old subject (subject number 2) were first shown a first image, followed immediately by a second image. The first and second images differed only in their degree of blur, with one image being blurrier than the other. After viewing the two images, the subjects were asked to indicate which image was blurrier. If the subjects responded correctly twice in a row, the difference in blur between the two images was reduced (making it more difficult to make a correct answer), and the visual training task was repeated.

[0153] Before training, the following parameters were measured for each subject: 1. The accuracy of responses in a working memory test (such as the test shown in Figure 12). 2. Subjects' mean response times in the working memory test. 3. Gamma wave coherence (connection strength) between visual processing areas of the brain (P7, P8) and cognitive areas of the brain (Fz). 4. P300 in the cognitive domain (Fz).

[0154] During training, gamma frequency power was measured in visual processing areas (P7, P8) and cognitive processing areas (Fz) during multiple visual training tasks, including the presentation of a first image followed immediately by a second image (measured in dB of gamma in the 30-49 Hz band). The first and second images were presented with different levels of blur, ranging from 10% to 2%. Frequency power was measured within 0.5 seconds of the presentation of the first image during the visual training task (this allows information processing in both visual processing areas (P7, P8) and cognitive processing areas (Fz)). The first and second images were randomly assigned to either high or low blur groups. The mean frequency power response at both P7+P8 and Fz was significantly elevated in response to the high blur image. This was observed in both Subject 1 and Subject 2, as shown in Figures 16E and 16F. Gamma power (30–50 Hz) was calculated 0.5 seconds (memory time) after the first stimulus (image display) and corrected using the baseline value 0.5 seconds before the stimulus (image display).

[0155] After training, the following parameters were reassessed for each subject: 1. The accuracy of responses in working memory tests. 2. Subjects' mean response times in the working memory test. 3. Gamma wave coherence (connection strength) between visual processing areas of the brain (P7, P8) and cognitive areas of the brain (Fz). 4. P300 in the cognitive domain (Fz).

[0156] This training resulted in many improvements in the subjects. Increased cognitive ability was observed, as evidenced by improved accuracy in the working memory test. Response times, on the other hand, remained substantially unchanged. Furthermore, coherence (strength of connection) between visual processing areas of the brain (P7, P8) and cognitive areas of the brain (Fz) also increased substantially, as shown in Figure 15A for subject 1 and Figure 15C for subject 2. Furthermore, this training also had a substantial positive effect on the P300, both in amplitude and velocity, as shown in Figures 16A and 16B for subject 1.

[0157] Figures 15A, 15B, 15C, and 15D show gamma coherence analysis of EEG measurements before (circled lines) and after (triangle lines) the blur task training session for two subjects: an 80-year-old (subject number 1), Figures 15A and 15B; and a 60-year-old (subject number 2), Figures 15C and 15D. EEG gamma coherence was calculated between frontal and parietal lobe sites (Figures 15A and 15C) and between two parietal lobe sites across hemispheres (Figures 15B and 15D). The results show increased (improved) coherence after training. Figures 15E, 15F, 15G, and 15H show improved cognitive performance after the working memory test. Figures 15E and 15F show an increase in accuracy, i.e., an increase or improvement in cognitive performance. In Figures 15G and 15H, response times are shown in milliseconds.

[0158] Figures 16A and 16B show the difference in P300 responses (measured at the frontal electrode Fz) before ( Figure 16A ) and after ( Figure 16B ) a blur task training session in an 80-year-old subject (subject #1) when target images (solid lines) and non-target images (dashed lines) were presented during a working memory (WM) task (similar to the task described in Figure 12 ). Stimuli (image displays) were presented for 2000 ms and lasted for 500 ms. Gray boxes indicate the duration of the stimuli (image displays). Figures 16C and 16D show the P300 amplitude ( Figure 16C ) and P300 latency ( Figure 16D ) of the 80-year-old subject (subject #1) before and after the blur task training session, similar to that shown in Figures 16A and 16B . As shown, the P300 amplitude increased and the P300 latency decreased after training.

[0159] Figure 17 shows the "Count Dogs" task. Twenty-one subjects were presented with a slideshow (0.5 Hz) of cartoon animal images and were asked to report the number of dogs (target images) they had seen at the end of the task.

[0160] Figures 18A and 18B show the P300 responses measured at Pz (Figure 18A) and Fz (Figure 18B) when target images (solid lines) and non-target images (dashed lines) were displayed in the "counting dogs" task shown in Figure 17. The results show that the P300 peak in response to target images was larger than the P300 peak in response to non-target images. This result was also observed at the frontal lobe electrode (Fz) and the occipital lobe electrode (Pz).

[0161] Various embodiments are disclosed herein. Features of a particular embodiment may be combined with features of other embodiments. Thus, a particular embodiment may be a combination of features from multiple embodiments.

[0162] While certain features of the invention have been illustrated and described herein, various modifications, substitutions, changes, and equivalents will occur to those skilled in the art. It is therefore to be understood that the appended claims are intended to cover all such modifications and variations that fall within the true spirit of the invention.

Claims

1. 1. A method for improving a user's cognitive performance, comprising: At least one session (S １ , S ２ , ..., S Ｍ ) to the user, Each session requires one or more responses from the user. ｓ visual training tasks, The method, wherein the visual training tasks are configured to induce an increase in brain frequency power based on the user's response to at least one of the visual training tasks displayed to the user.

2. 10. The method of claim 1, K ｓ A session S containing time steps １ At a given time step N (N=K, l≦K≦K ｓ receiving and / or collecting a response profile of the user to the visual training task displayed at the given time step (N=K); The session S １ analyzing the response profile of the user received at the given time step (N=K) and optionally at any previous time step (N<K); The session S １ The time step of K≦K ｓ and / or until it is determined that the response profile has reached a predetermined threshold. １ and repeating the step of displaying the visual training task to the user at a next time step (K=K+1) of

3. 10. The method of claim 1, K ｓ time steps (N=K, l≦K≦K ｓ ) the session S １ The last time step (N = K ｓ ) In the session S １ time step N (N=K, l≦K≦K ｓ receiving and / or collecting a response profile of the user to a plurality of the visual training tasks displayed in a visual training task table; The session S １ the last time step (N=K ｓ analyzing the response profile of the user received via The session S １ until a predetermined number of time steps have been reached and / or the response profile is determined to have reached a predetermined threshold. ２ Next time step N (N=K, l≦K≦K ｓ ) repeating the step of displaying the visual training task to the user.

4. 10. The method of claim 1, The method, wherein the inducing an increase in brain frequency power comprises an increase in gamma wave power.

5. 10. The method of claim 1, Inducing an increase in the frequency power of the brain includes: Increased P300 positive EEG component, a reduction in P300 latency of a brain response after the user responds to the visual training task displayed to the user.

6. 10. The method of claim 1, A method in which the visual training task is selected to induce an increase in frequency power of the brain based on the user's response to at least one visual training task and / or visual training stimulus image that is not displayed to the user by the method.

7. 10. The method of claim 1 or 6, The method, wherein the visual training task includes distinguishing between displays of target and non-target images.

8. 10. The method of claim 1, The increase in brain frequency power induced based on the response to the visual training task displayed to the user is The method is configured to improve a condition of a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

9. 10. The method of claim 1, The method further comprises use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

10. 1. A method for improving brain coherence between visual and cognitive domains of a user, comprising: At least one session (S １ , S ２ , ..., S Ｍ ) to the user, Each session is ｓ visual training tasks, A method wherein the visual training tasks are configured to induce an increase in brain gamma wave power in a local region based on the user's response to at least one of the visual training tasks displayed to the user.

11. 11. The method of claim 10, K ｓ A session S containing time steps １ At a given time step N (N=K, l≦K≦K ｓ receiving and / or collecting a response profile of the user to the visual training task displayed at the given time step (N=K); The session S １ analyzing the response profile of the user received at the given time step (N=K) and optionally at any previous time step (N<K); The session S １ The time step of K≦K ｓ and / or until it is determined that the response profile has reached a predetermined threshold. １ and repeating the step of displaying the visual training task to the user at a next time step (K=K+1) of

12. 11. The method of claim 10, K ｓ time steps (N=K, l≦K≦K ｓ ) the session S １ The last time step (N = K ｓ ) In the session S １ time step N (N=K, l≦K≦K ｓ receiving and / or collecting a response profile of the user to a plurality of the visual training tasks displayed in a visual training task table; The session S １ the last time step (N=K ｓ analyzing the response profile of the user received via The session S １ until a predetermined number of time steps have been reached and / or the response profile is determined to have reached a predetermined threshold. ２ Next time step N (N=K, l≦K≦K ｓ ) repeating the step of displaying the visual training task to the user.

13. 11. The method of claim 10, The visual training task comprises: Increased P300 positive EEG component, and a shortening of P300 latency of a brain response after the user responds to the visual training task displayed to the user.

14. 11. The method of claim 10, A method in which the visual training task is selected to induce an increase in frequency power of the brain based on the user's response to at least one visual training task and / or visual training stimulus image that is not displayed to the user by the method.

15. 11. The method of claim 10, The increase in brain frequency power induced based on the response to the visual training task displayed to the user is The method is configured to improve a condition of a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

16. 11. The method of claim 10, The method further comprises use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

17. 1. A method for improving a user's cognitive performance, comprising: At least one session (S １ , S ２ , ..., S Ｍ ) to the user, Each session is ｓ visual training stimulus images, A method wherein the visual training stimulus images are configured to induce an increase in brain frequency power based on the user's response to at least one of the visual training stimulus images displayed to the user.

18. 18. The method of claim 17, The method, wherein the inducing an increase in brain frequency power comprises an increase in gamma wave power.

19. 18. The method of claim 17, Inducing an increase in the frequency power of the brain includes: Increased P300 positive EEG component, and a shortening of the P300 latency of the brain response after the user responds to the visual training stimulus image displayed to the user.

20. 18. The method of claim 17, A method in which the visual training stimulus images are selected to induce an increase in frequency power in the brain based on the user's response to at least one visual training task and / or visual training stimulus image that is not displayed to the user by the method.

21. 18. The method of claim 17, The increase in brain frequency power induced based on the response to the visual training stimulus image displayed to the user is The method is configured to improve a condition of a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

22. 18. The method of claim 17, The method further comprises use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

23. 1. A method for improving brain coherence between visual and cognitive domains of a user, comprising: At least one session (S １ , S ２ , ..., S Ｍ ) to the user, Each session is ｓ visual training stimulus images, A method wherein the visual training stimulus images are configured to induce an increase in brain gamma wave power in a local region based on the user's response to at least one of the visual training stimulus images displayed to the user.

24. 24. The method of claim 23, The visual training stimulus image is Increased P300 positive EEG component, and a shortening of the P300 latency of a brain response after the user responds to the visual training stimulus image displayed to the user.

25. 24. The method of claim 23, A method in which the visual training stimulus images are selected to induce an increase in frequency power in the brain based on the user's response to at least one visual training task and / or visual training stimulus image that is not displayed to the user by the method.

26. 24. The method of claim 23, The increase in brain frequency power induced based on the response to the visual training stimulus image displayed to the user is The method is configured to improve a condition of a patient suffering from at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

27. 24. The method of claim 23, The method further comprises use in the treatment of at least one of cognitive decline, motor decline, Alzheimer's disease, depression, schizophrenia, ADHD, dyslexia, Parkinson's disease, multiple sclerosis, or any combination thereof.

28. 1. A system configured to display an image to a user, comprising: At least one processor configured to perform the method steps of any of claims 1 to 27; and at least one display device configured to display visual training tasks and / or visual training stimulus images to a user.

29. 29. The system of claim 28, The system further comprises at least one input device configured to collect and analyze the user's responses to the images displayed to the user at any given time step (N=K).

30. 29. The system of claim 28, The system further comprises a device selected from a computer, a smartphone, a tablet, and any combination thereof.

31. 29. The system of claim 28, The system further comprises at least one of a data storage for storing input from the user and an analysis of the input, an input device, a speaker device, a microphone device, and a computer mouse.

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