PERSONALIZED NEUROFEEDBACK TRAINER FOR OPTIMIZED BRAIN FUNCTION AND MENTAL WELL-BEING

Abstract

ABSTRACT The present disclosure introduces a personalized neurofeedback trainer for optimized brain function and mental well-being 100 which is an AI-powered system designed to optimize brain function and enhance mental well-being by providing real-time, adaptive neurofeedback based on the user's brain activity. Utilizing EEG sensors 102 to capture brainwave data, the system processes this information through the AI module 104, which generates personalized training protocols tailored to the user's cognitive goals. The real-time feedback module 106 delivers dynamic feedback in the form of visual, auditory, or tactile cues, engaging the user in interactive exercises and games 112. The system also includes cloud-based data storage and analytics 110 for securely storing user data and providing long-term insights into cognitive and emotional improvement. Through the machine learning based training protocol 114, the system continuously adapts the training to the user’s progress, making it highly personalized and effective.

Specification

Description:Personalized Neurofeedback Trainer for Optimized Brain Function and Mental Well-being
TECHNICAL FIELD
[0001] The present innovation relates to the field of neurofeedback technology, specifically utilizing AI-powered, real-time brain activity analysis through EEG sensors to optimize cognitive and emotional well-being.

BACKGROUND

[0002] Neurofeedback training has emerged as a promising technique for improving cognitive functions and emotional well-being by providing users with real-time feedback on their brain activity. However, despite the advancements in this field, existing solutions face several limitations. Conventional neurofeedback systems rely on generic feedback mechanisms that fail to account for the unique brain activity patterns of individual users. This one-size-fits-all approach limits the efficacy of these systems, as users are often unable to achieve optimal brain training outcomes. Additionally, many neurofeedback systems are either prohibitively expensive or require professional supervision, restricting their accessibility for wider populations. These challenges create a need for a more personalized, user-friendly, and cost-effective neurofeedback system.

[0003] Several patents and technologies exist in the neurofeedback training space, offering various approaches to brain training. For example, US Patent 10,434,111 and US Patent 9,855,221 describe systems that offer personalized and real-time neurofeedback respectively. Additionally, research papers like "Personalized neurofeedback training for anxiety disorders" (2020) and "Machine learning-based neurofeedback for cognitive enhancement" (2019) explore the use of neurofeedback to address specific cognitive and emotional issues. However, these existing patents and research papers fall short in certain key areas. Most notably, they do not integrate advanced AI-powered personalization, machine learning, real-time feedback mechanisms, and cloud-based data storage and analytics into a single, cohesive system.

[0004] The Personalized Neurofeedback Trainer differentiates itself from existing technologies by offering a novel combination of AI-driven personalization, machine learning algorithms, and cloud-based data storage. Unlike traditional neurofeedback systems, this invention tailors training protocols to each user's unique brain activity patterns, dynamically adjusting feedback in real-time based on individual progress. This personalized approach ensures a more effective and engaging training experience. Moreover, the integration of cloud-based analytics allows users to track their progress over time and gain deeper insights into their cognitive and emotional development.

[0005] The invention overcomes the drawbacks of existing systems by offering a non-invasive, affordable, and accessible sytem that does not require professional supervision. Its AI-powered personalization, adaptive feedback, and machine learning capabilities make it a standout in the field, addressing the lack of user-specific customization and long-term engagement present in current neurofeedback technologies.

OBJECTS OF THE INVENTION

[0006] The primary object of the invention is to optimize brain function and mental well-being through personalized neurofeedback training based on real-time brain activity data.

[0007] Another object of the invention is to provide an AI-powered system that personalizes neurofeedback protocols for each user, adapting to individual brain patterns and cognitive goals.

[0008] Another object of the invention is to offer a non-invasive, user-friendly neurofeedback system that can be used without professional supervision, making brain training accessible to a wider population.

[0009] Another object of the invention is to integrate machine learning algorithms to analyze brain activity and continuously adapt training protocols to ensure long-term cognitive and emotional improvement.

[00010] Another object of the invention is to provide real-time feedback through visual, auditory, and tactile cues, enhancing user engagement and ensuring more effective brain training sessions.

[00011] Another object of the invention is to use cloud-based data storage and analytics to securely track user progress over time, enabling users to monitor their cognitive and emotional development.

[00012] Another object of the invention is to address the limitations of conventional neurofeedback systems by offering a fully personalized, adaptive training experience that optimizes neuroplasticity for better focus, attention, and emotional regulation.

[00013] Another object of the invention is to create a cost-effective neurofeedback training system that is scalable and can be used by individuals, clinicians, and researchers alike.

[00014] Another object of the invention is to promote better cognitive health and emotional well-being through an innovative system that leverages the latest advancements in artificial intelligence and neurofeedback.

[00015] Another object of the invention is to enhance long-term mental performance by offering a system that evolves with the user's progress, providing adaptive training protocols that keep pace with the user's needs.

SUMMARY OF THE INVENTION

[00016] In accordance with the different aspects of the present invention, personalized neurofeedback trainer for optimized brain function and mental well-being is presented. It is an AI-powered system designed to optimize brain function and mental well-being by using real-time EEG data to provide adaptive, personalized feedback. It leverages machine learning algorithms to analyze brain activity and continuously adjust training protocols, offering tailored exercises, interactive games, and meditation sessions. The system features cloud-based data storage and analytics, allowing users to track their cognitive and emotional progress over time. This non-invasive, user-friendly technology addresses limitations of conventional neurofeedback systems, making it accessible and effective for a wide range of users. Its innovative approach promotes neuroplasticity, enhancing focus, memory, and emotional regulation.

[00017] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00018] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00019] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00020] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00021] FIG. 1 is component wise drawing for personalized neurofeedback trainer for optimized brain function and mental well-being.

[00022] FIG. 2 depicts system architecture of personalized neurofeedback trainer for optimized brain function and mental well-being.

[00023] FIG 3 depicts high level dataflow in personalized neurofeedback trainer for optimized brain function and mental well-being.

[00024] FIG 4 is working methodology personalized neurofeedback trainer for optimized brain function and mental well-being.


DETAILED DESCRIPTION

[00025] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00026] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of personalized neurofeedback trainer for optimized brain function and mental well-being and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00027] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00028] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00029] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00030] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00031] Referring to Fig. 1, personalized neurofeedback trainer for optimized brain function and mental well-being 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of EEG sensors 102, AI module 104, real-time feedback module 106, user interface 108, cloud-based data storage and analytics 110, interactive exercises and games 112 and machine learning based training protocol 114.

[00032] Referring to Fig. 1, the present disclosure provides details of personalized neurofeedback trainer for optimized brain function and mental well-being 100 which is an innovative, AI-powered system designed to optimize brain function and enhance mental well-being. It utilizes EEG sensors 102 to monitor brain activity in real time, with the AI module 104 analysing this data to create personalized neurofeedback protocols. The system 100 provides adaptive, real-time feedback through the real-time feedback module 106, engaging users with interactive exercises and games 112 via an intuitive user interface 108. The system stores user progress and data securely in the cloud-based data storage and analytics 110, enabling continuous improvement. The machine learning based training protocol 114 ensures that the neurofeedback training evolves with the user's progress, making the experience highly effective and personalized. This non-invasive sytem promotes cognitive enhancement, emotional regulation, and neuroplasticity.

[00033] Referring to Fig. 2 and Fig 3, personalized neurofeedback trainer 100 stores key information like name, age, and cognitive goals, allowing the system to tailor each session to the user's specific needs. Users can select their desired Training Mode-such as focus, relaxation, or emotional regulation through user interface 108 ensuring personalized sessions. During training, real-time brain activity is displayed in the EEG Signal Graph captured through sensor 102, while the Real-Time Feedback module 106 provides adaptive feedback via visual, auditory, or tactile cues. This enables users to actively adjust their mental state based on immediate feedback. Additionally, the system 100 records session history, including date, time, and session duration, alongside tracking training goals to measure cognitive and emotional development over time. Together, these elements ensure a customized, user-centric training experience that evolves with the user's progress.

[00034] Referring to Fig 1, personalized neurofeedback trainer 100 is provided with EEG sensors 102, which play a critical role in capturing real-time brain activity. These sensors 102 monitor the electrical patterns in the user's brain and convert them into usable data for analysis. EEG sensors 102 operate by detecting different EEG frequency bands such as delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-100 Hz). These signals are processed using techniques like filtering and artifact removal, ensuring accurate readings. The data collected by EEG sensors 102 is crucial for generating personalized neurofeedback protocols. The sensors work in conjunction with the AI module 104, providing the raw brainwave data necessary for real-time analysis and feedback generation.

[00035] Referring to Fig 1, personalized neurofeedback trainer 100 is provided with the AI module 104, which serves as the core engine of the system by personalizing neurofeedback protocols based on the brain activity data collected by the EEG sensors 102. The AI module 104 utilizes machine learning algorithms, including supervised learning models like decision trees and linear regression, to process EEG data. These algorithms adapt to the user's progress over time, updating the training protocols to reflect the user's current cognitive state. Parameters such as the user's profile, including age, goals, and brain activity data, are fed into the AI module 104 through user interface 108 to generate personalized recommendations. The AI module 104 works closely with the machine learning based training protocol 114 to dynamically adjust the training intensity and feedback.

[00036] Referring to Fig 1, personalized neurofeedback trainer 100 is provided with the real-time feedback module 106, which delivers adaptive feedback to the user based on their brain activity. This module generates feedback in various formats, including visual, auditory, and tactile cues, allowing the user to understand their brain states. The intensity and type of feedback can be adjusted dynamically based on real-time EEG data. The real-time feedback module 106 is directly connected to the AI module 104, ensuring that the feedback corresponds accurately to the user's cognitive performance. This module is essential for reinforcing positive brainwave patterns through a reward-based mechanism and penalty for undesired brain activity, which can be adjusted with different thresholds for desired brain activity.

[00037] Referring to Fig 1, personalized neurofeedback trainer 100 is provided with the user interface 108, designed to offer a user-friendly platform for interacting with the neurofeedback system. The user interface 108 allows users to input their demographic information, training goals, and preferences, while also displaying real-time EEG signal graphs, progress tracking, and session history. Users can select different training modes, such as focus or relaxation, and receive real-time updates on their performance. The user interface 108 is connected to both the real-time feedback module 106 and the cloud-based data storage and analytics 110, providing a seamless experience for users to monitor and adjust their sessions.

[00038] Referring to Fig 1, personalized neurofeedback trainer 100 is provided with the cloud-based data storage and analytics 110, which securely stores user data such as brain activity logs, training sessions, and progress reports. This component plays a critical role in enabling long-term tracking and analytics of the user's cognitive performance. The cloud-based data storage and analytics 110 uses advanced data visualization techniques to present insights into brain activity and cognitive development, allowing users, trainers, or researchers to review and optimize performance. Additionally, it supports data security through encryption and access controls, ensuring that sensitive user data remains confidential. This module works alongside the AI module 104 to refine training protocols based on historical data.

[00039] Referring to Fig 1, personalized neurofeedback trainer 100 is provided with interactive exercises and games 112, which are customized for each user to promote cognitive improvement and emotional regulation. These games and exercises are designed to engage the user actively while reinforcing positive brainwave patterns through real-time feedback. The system adjusts the complexity and nature of the exercises based on the user's progress, ensuring continuous engagement. The interactive exercises and games 112 work in tandem with the real-time feedback module 106 and the AI module 104 to adapt the training in real-time, making the system both effective and enjoyable for users.

[00040] Referring to Fig 1, personalized neurofeedback trainer 100 is provided with the machine learning based training protocol 114, which serves as the foundation for generating personalized neurofeedback sessions. This component utilizes machine learning algorithms to process EEG data and identify cognitive improvement areas. The machine learning based training protocol 114 adapts in real time, adjusting the complexity and difficulty of exercises based on the user's progress. Additionally, it refines the feedback mechanisms, making sure that both positive and negative brainwave patterns are addressed appropriately. This component 114 works closely with the AI module 104 and cloud-based data storage and analytics 110 to ensure that training remains personalized and adaptive over time


[00041] Referring to Fig 4, there is illustrated method 200 for personalized neurofeedback trainer for optimized brain function and mental well-being 100. The method comprises:

At step 202, method 200 includes the user inputting details such as age, cognitive goals, and preferences into the user interface 108;

At step 204, method 200 includes the EEG sensors 102 monitoring the user's brain activity in real time and capturing brainwave data across multiple EEG frequency bands;

At step 206, method 200 includes the EEG sensors 102 transmitting the collected brain activity data to the AI module 104 for real-time analysis;

At step 208, method 200 includes the AI module 104 processing the brain activity data using machine learning algorithms to identify cognitive and emotional areas for improvement;

At step 210, method 200 includes the AI module 104 generating a personalized neurofeedback training protocol based on the user's unique brain activity and cognitive goals;

At step 212, method 200 includes the real-time feedback module 106 providing visual, auditory, or tactile feedback to the user, based on the personalized protocol generated by the AI module 104;

At step 214, method 200 includes the user engaging in interactive exercises and games 112, which are customized in real-time based on their brain activity data and progress;

At step 216, method 200 includes the machine learning based training protocol 114 adapting the difficulty and intensity of the training as the user's brain activity evolves throughout the session;

At step 218, method 200 includes the cloud-based data storage and analytics 110 securely storing the user's brain activity data, training sessions, and progress reports for future analysis and session customization;

At step 220, method 200 includes the user reviewing progress via the user interface 108, which provides detailed feedback and recommendations for future neurofeedback sessions, further refining the training experience.

[00042] The Personalized Neurofeedback Trainer 100 offers significant advancements over existing neurofeedback systems by integrating advanced technologies like AI, machine learning, and real-time feedback. Existing technologies, such as traditional neurofeedback systems, neurostimulation techniques (e.g., tDCS, TMS), brain-computer interfaces, and mobile brain training apps, exhibit several limitations. These include a lack of personalization, limited accessibility, insufficient scientific validation, and inadequate feedback mechanisms.

[00043] Unlike traditional systems that provide generic, one-size-fits-all feedback, the personalized neurofeedback trainer 100 uses AI-powered personalization via the AI module 104, which tailors neurofeedback protocols based on real-time brain activity captured by EEG sensors 102. This dynamic personalization is not possible in current systems that rely on static protocols and provide limited feedback. Moreover, while existing technologies offer minimal real-time feedback, typically visual or auditory cues, the real-time feedback module 106 in this system provides multimodal feedback (visual, auditory, and tactile), dynamically adapting based on the user's brain activity. This leads to better engagement and training efficacy.

[00044] Another critical improvement is the inclusion of cloud-based data storage and analytics 110, allowing for secure data storage, long-term tracking, and detailed performance analytics. Unlike traditional systems that lack robust data solutions, this feature enables users and clinicians to monitor cognitive progress over time and adjust training protocols accordingly. Furthermore, existing systems are often invasive, expensive, and require professional supervision. In contrast, the personalized neurofeedback trainer 100 is designed to be non-invasive and user-friendly, providing an affordable, self-directed solution that is accessible to a broader population.

[00045] The invention also features interactive exercises and games 112, keeping users engaged with personalized challenges that are continually adapted by the machine learning based training protocol 114. This contrasts with current systems, which often fail to sustain user motivation due to non-adaptive, static feedback mechanisms. By incorporating scientifically validated methods and engaging the user through interactive activities and meditation sessions, the invention ensures long-term cognitive and emotional benefits.

[00046] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00047] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00048] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. A personalized neurofeedback trainer for optimized brain function and mental well-being 100 comprising of
EEG sensors 102 to monitor and capture real-time brain activity data;
AI module 104 to analyze brain activity and generate personalized training protocols;
real-time feedback module 106 to deliver adaptive feedback through visual, auditory, or tactile cues;
user interface 108 to allow users to input information and interact with the system;
cloud-based data storage and analytics 110 to securely store user data and track cognitive progress;
interactive exercises and games 112 to engage users with personalized cognitive and emotional training activities and
machine learning based training protocol 114 to adapt training sessions based on the user's brain activity and progress.

2. The personalized neurofeedback trainer for optimized brain function and mental well-being 100 as claimed in claim 1, wherein AI module 104 uses machine learning algorithms to analyze the user's brain activity in real time and adapt the training protocols dynamically based on the user's progress.

3. The personalized neurofeedback trainer for optimized brain function and mental well-being 100 as claimed in claim 1, wherein real-time feedback module 106 provides feedback based on reinforcement learning techniques to reward positive brain activity patterns and penalize undesirable patterns.

4. The personalized neurofeedback trainer for optimized brain function and mental well-being 100 as claimed in claim 1, wherein cloud-based data storage and analytics 110 securely stores brain activity data and provides detailed reports and visualizations, enabling users to monitor their cognitive improvement over time.

5. The personalized neurofeedback trainer for optimized brain function and mental well-being 100 as claimed in claim 1, wherein machine learning based training protocol 114 adjusts the difficulty and intensity of the neurofeedback exercises based on the user's brain activity and cognitive progress.

6. The personalized neurofeedback trainer for optimized brain function and mental well-being 100 as claimed in claim 1, wherein AI module 104 incorporates a neural network for enhanced personalization, analyzing the user's brain activity patterns to identify areas for improvement in cognitive function and emotional regulation.

7. The personalized neurofeedback trainer for optimized brain function and mental well-being 100 as claimed in claim 1, wherein real-time feedback module 106 provides multimodal feedback, enabling the user to receive visual, auditory, and tactile cues during the neurofeedback session.

8. The personalized neurofeedback trainer for optimized brain function and mental well-being 100 as claimed in claim 1, wherein machine learning based training protocol 114 adapts based on user feedback, optimizing training effectiveness by continuously learning from the user's cognitive patterns and behavioral responses.

9. The personalized neurofeedback trainer for optimized brain function and mental well-being 100 as claimed in claim 1, wherein cloud-based data storage and analytics 110 integrates with external health platforms to provide additional insights and recommendations for improving cognitive performance and emotional well-being
10. The personalized neurofeedback trainer for optimized brain function and mental well-being 100 as claimed in claim 1, wherein method comprises of
user inputting details such as age, cognitive goals, and preferences into the user interface 108;

EEG sensors 102 monitoring the user's brain activity in real time and capturing brainwave data across multiple EEG frequency bands;

EEG sensors 102 transmitting the collected brain activity data to the AI module 104 for real-time analysis;

AI module 104 processing the brain activity data using machine learning algorithms to identify cognitive and emotional areas for improvement;

AI module 104 generating a personalized neurofeedback training protocol based on the user's unique brain activity and cognitive goals;

real-time feedback module 106 providing visual, auditory, or tactile feedback to the user, based on the personalized protocol generated by the AI module 104;

user engaging in interactive exercises and games 112, which are customized in real-time based on their brain activity data and progress;

machine learning based training protocol 114 adapting the difficulty and intensity of the training as the user's brain activity evolves throughout the session;
cloud-based data storage and analytics 110 securely storing the user's brain activity data, training sessions, and progress reports for future analysis and session customization;

user reviewing progress via the user interface 108, which provides detailed feedback and recommendations for future neurofeedback sessions, further refining the training experience.

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