Description:Field of the Invention
The proposed invention relates to the Neurofeedback Meditation App which combines the techniques of meditation with improvements in neurofeedback technology in order to measure brainwave activity in real-time and improve meditation experiences.
Objective of this invention
The primary objective is to give users access to real-time information about their brainwave activity while they are meditating. The app's goal is to produce a dynamic and customized meditation experience by seamlessly combining the meditation techniques with modern neurofeedback technology. The software tries to identify relationships between different mental states and certain meditation techniques by precisely measuring and tracking users' brainwave patterns, which improves the quality of meditation sessions. The software allows users to obtain a profound understanding of their inner mental processes and adjust their meditation practices to attain desired goals by providing quick visual feedback. The app's user-friendly design guides users through calibration and meditation sessions, creating an effortless and enjoyable experience.
Background of the Invention
The origins of the idea can be traced to the integration of cutting-edge neurofeedback technology and traditional mindfulness techniques. Even while it is helpful, traditional meditation frequently fails to give the practitioner access to quick insights regarding their mental states and development. By offering an innovative solution that makes use of real-time brainwave analysis, our technology aims to close that gap.
For instance, US005899867A discloses the system for electroencephalographic (EEG) neurofeedback training. It includes an EEG monitor, electrodes, a computer, and software for monitoring, recording, analyzing, and displaying EEG signals. The system generates various types of screen displays based on the received EEG signals, including waveforms, frequency spectra, phase-space plots, compressed spectral arrays, bar graphs, thermometers, and trend plots. It also includes displays such as two-dimensional plots, highway-type displays, pacman-type displays, and facial expression displays. The system has two operating modes: Learn mode and Train mode, and allows users to receive neurofeedback training by observing and controlling the displayed graphic images.
Similarly, US20050197556A1 relates to a neurofeedback system for EEG/ECG readings described by this invention as adaptable. For precise electrode implantation, it has an adjustable headband or cross straps with electrode holders. The electrode holders have apertures for lead flow and pockets or fasteners for securing electrodes. There is no need for harsh gels because the design conveniently fits different head sizes and shapes. It can be utilized for training, testing, and diagnosis purposes and enables versatility in electrode placement. The gadget guarantees enough electrical contact for accurate readings.
US2014O163410A1 also relates to a neurofeedback system and method that can be used at home or remotely. It provides automatic or semi-automatic neurofeedback treatment, allowing therapists or the system to construct treatment protocols remotely. The device measures brain activity via sensors on the patient's skull and sends the data to a central computer for analysis. The patient receives feedback through computer-based programs/activities. Based on recorded brain activity, the technology can design therapy procedures automatically. It supports a variety of computer-based activities, including word processing, social networking, and online gaming. The technology is inexpensive, wireless, and can treat numerous patients at the same time. It has benefits such as remote installation, less therapist interaction, and customized therapy.
Muse is a brain-sensing headband and software that gives users instant feedback on their brain activity while they are meditating. Brainwave patterns are detected by the EEG sensors in the Muse headband, which then transmits the information to the companion app. The user is then guided into a calmer and more concentrated state during meditation by the app's auditory and visual signals, which are based on the user's brainwave activity. There is also Helium, an app that provides guided meditation sessions and makes use of biofeedback to improve the experience. To gauge a user's level of relaxation, it offers capabilities like real-time heart rate tracking and physiological signal monitoring. Based on this information, the app creates meditation exercises that are tailored to the user's requirements in order to assist them reach a deeper state of meditation.
The previous inventions discussed various goals of utilizing technology to improve meditation through neurofeedback. However, their distinct characteristics and goals show their contrasts. With an emphasis on adaptability, remote treatment, and electrode placement, the inventions provide complete systems for neurofeedback training, diagnosis, and therapy. Muse is a meditation app that uses EEG sensors to help users achieve greater calmness during private sessions. The workouts in Helium are customized depending on physiological signals and include guided meditation with biofeedback. In contrast, Neurofeedback Meditation App stands out with its advanced data analysis, machine learning, and personalized techniques. In order to provide users with long-lasting mindfulness benefits, it attempts to build a personalized meditation mission that develops over time and adjusts to their particular brainwave patterns. This makes your software stand out since it has a long-term focus on enhancing mindfulness through data-driven customization.
Summary of the Invention
The present invention offers a creative blend of modern technology and traditional meditation techniques, with the goal of improving users' meditation experiences by providing real-time information about their brainwave activity. This program allows users to track their brainwave patterns during meditation sessions and receive customized feedback to improve their practices. It provides users with quick information to assist them comprehend their thinking processes, allowing them to make adjustments for desired outcomes. The software offers a user-friendly measurement and meditation interface along with interactive feedback to help users better relate their internal mental states to their surroundings. Users receive thorough analysis following each session to track their development and improve their meditation practices. The software prioritizes data protection and privacy while potentially advancing scientific investigation into the effects of meditation on brainwave activity and mental well-being.

Brief Description of Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure-1:Flowgorithm representing the work flow of neurofeedback meditation app
Detailed Description of the Invention
The Neurofeedback Meditation App provides an innovative combination of traditional contemplative ways and modern technical advancements. A new era of mental wellbeing is introduced by this significant application, which skillfully combines the practice of meditation with the most recent advancements in neurofeedback research. The software effectively integrates cutting-edge technology with traditional knowledge to create a light that points users in the direction of deeper mindfulness, sharper concentration, and more relaxation. This app stands out because of the skillful integration of cutting-edge Electroencephalogram (EEG) sensors and advanced signal processing algorithms. Together, these technical elements offer individuals an unmatched chance to explore their own brainwave activity in real time as they practice meditation. This feature provides a unique and informative look into one's interior cognitive environment, building a profound connection between mind and machine. The design of this app is based on the dynamic interaction between the practice of meditation and technological capabilities, allowing users to not only observe but also actively modify their meditation technique in response to their brainwave data. This allows for the cultivation of an ideal and profoundly enlightening meditation experience. The Neurofeedback Meditation App essentially serves as a witness to the amazing opportunities that emerge when tradition and innovation peacefully collide for the improvement of mental well-being.
The app provides live visual representations of users' meditative brain activity. Creating a real-time neurofeedback visualization within the Neurofeedback Meditation App requires a sophisticated integration of data processing, visualization techniques, and user interface design. The first step in the procedure is the collection of unfiltered brainwave data using EEG devices during meditation sessions. The quality and significance of this raw data are then improved by a series of critical preprocessing activities. Low-pass, high-pass, and bandpass filters are used combined to successfully remove noise and artifacts. These filters are essential for separating important brainwave patterns from irrelevant noise. Then, different brainwave frequencies, including alpha, beta, and theta waves, which are individually connected to different mental states, are identified from the EEG data. The conversion of this processed EEG data into a user-interactive and comprehensible format is the core of the visualization process. Both time domain and frequency domain analysis are included in the implementation. The software analyzes variations in brainwave patterns over the course of a meditation session in the temporal domain to reveal shifts in mental states. Users can assess their initial level of serenity, relaxation, or attention and see how these states change over time using this time-based analysis. The Fourier Transform is the main player in the frequency domain. This mathematical procedure, analogous to separating unique musical notes from a song, translates the fundamental frequency components of the EEG data. The software uses Fourier Transform to determine which brainwave frequencies are most common at various points during meditation. The software can provide users with real-time feedback on their mental states thanks to this understanding. The procedure involves sorting the EEG data into preset frequency bins and transforming the EEG data into a representation that emphasizes the strengths of different brainwave frequencies. This serves as the foundation for building a power distribution graph that shows the relative dominance of various brainwave frequencies.
The integration of preprocessed EEG data into the app's frontend is necessary to achieve real-time visualization. This is possible with technologies such as WebSockets or real-time data streaming libraries. These innovations create an ongoing link between the backend and the frontend, enabling the constant transfer of EEG data. The software can dynamically update graphics as new data comes in thanks to this seamless connectivity. For user interpretation, the selection of graphical components is essential. For this, the potent JavaScript library D3.js may be used. Line graphs that show the evolution of brainwave amplitudes over time and colored bars that show the amplitudes of different brainwave frequencies are examples of visualization components. These visualizations' dynamic character is achieved by data prepping, D3.js configuration, the construction of graphical elements, ongoing data updates, and re-rendering techniques.
The app makes use of a database of several meditation techniques connected to brainwave patterns. It uses machine learning to assess users' prior brainwave data and create meditation routines that are tailored to their individual interests and mental states. This results in tailored meditation experiences that are in line with user goals. The initial phase in this process is called pattern recognition. Different patterns relating to different mental states during meditation, such as calmness, focus, restlessness, or distraction, can be found by evaluating extracted features from users' brainwave data. This serves as the foundation for a deeper comprehension of users' meditation experiences. Support Vector Machines (SVM) and neural networks are emerging as promising pattern recognition model solutions to do this. Support Vector Machines are appropriate for the goal of your app because they are interpretable and excel with smaller datasets. They are excellent at categorizing various mental states using extracted EEG data, which helps users better understand their meditation experiences. The next stage is to link particular meditation methods to specific brainwave patterns. This entails connecting certain meditation techniques that produce or enhance desirable mental states with related brainwave frequencies (alpha, theta, and beta). This mapping can be put into a database that shows the relationship between efficient meditation methods and brainwave patterns.
Delivering customized meditation programs relies greatly on recommendation logic. This logic suggests suitable meditation approaches in real-time based on user choices, study of brainwave patterns, and historical data. It makes use of the categorization abilities of Support Vector Machines to categorize the mental states of users and the power of neural networks to recognize patterns for deeper associations. By giving customers the option to tailor their meditation experience, user preferences are taken into account. This covers preferences for meditation type, timing, duration, and background noise. The software makes the meditation experience more interesting and relevant by combining user preferences and customizing recommendations to fit individual interests. The app's progress monitoring feature enables it to track users' progress with meditation over time. The software tracks trends and advancements in users' meditation practices by gathering and examining previous brainwave pattern data. This data is then used to generate educated session recommendations, ensuring that users are given advice that is consistent with their development and objectives. The implementation of the data analysis, recommendation logic, and progress tracking components requires the use of tools such as statistical analysis software (NumPy, SciPy), programming languages (Python, Java), database management systems (MySQL), and database management systems (MySQL). Within the Neurofeedback Meditation App, this complete approach to customized meditation sessions delivers a holistic and user-centric meditation experience.
Audios for guided meditation are provided by the app and are timed with the neurofeedback visualization. These audios, which are provided by knowledgeable teachers, improve meditation by matching users' brainwave rhythms. incorporation of audio guidance adds a transformative layer to the meditation experience by fusing auditory and visual components for a deeper and more productive practice. To ensure a smooth and rewarding meditation journey, the deployment of this function entails numerous crucial procedures. Making a Guided Meditation Library is the main component of this service. This library ought to provide a wide range of instruction in guided meditation from qualified teachers. These sessions should support a variety of meditation practices, including loving-kindness, body scan, and mindfulness, and should also support varying session lengths. The materials available in the library ought to cover a range of meditation objectives, such as stress reduction, improved attention, and relaxation. The content of the guided meditations should be created to appeal to the unique preferences and goals of each user. Synchronization between the audio and the neurofeedback visualization is essential to achieve a seamless harmony. The auditory direction should be timed to coincide with the visual indications provided by the neurofeedback visualization. For instance, the audio advice should offer suggestions that promote deep relaxation and the release of tension if the visualization shows a condition of calm brainwave patterns. By utilizing both aural and visual signals to direct users toward their desired mental states, this coordination increases the efficiency of the meditation practice.
The ability to customize the audio advice feature is a crucial component. Users ought to have the ability to tailor their meditation experience to suit their tastes. This involves offering customers a selection of meditation teachers, methods, and session lengths. By providing a variety of audio guides, users can select the meditation material that most closely matches their personal meditation preferences and goals, creating a personalized and interesting meditation journey. The creation of high-quality guided meditation audio tracks requires the use of audio recording and editing programs like Audacity and Adobe Audition, or GarageBand in terms of implementation tools and resources. To ensure the creation of effective and compelling audio content that is in line with the objectives of the app, collaboration with knowledgeable meditation instructors is imperative. To enable the smooth integration of the guided meditation audios into the app's UI, the app development platform itself, such as React Native should have audio playing functionalities.
Therefore, the app provides a comprehensive and sophisticated platform that empowers users to achieve improved relaxation, focus, and mindfulness through an improved meditation experience. This is accomplished by seamlessly integrating cross-platform development, signal processing techniques, machine learning algorithms, real-time data transmission, user preferences, and interactive elements.
Advantages of the proposed model,
The proposed NeuroFeedback Meditation model has several potential advantages that can contribute to enhancing human well-being and providing a unique meditation experience. Here are some of the advantages of this model:
By using real-time EEG data to assess the user's mental state, the application can offer tailored meditation sessions that match the individual's current needs. This personalization can lead to more effective meditation outcomes and greater user satisfaction. The incorporation of EEG technology allows for objective measurement of brainwave activity, giving users concrete feedback about their mental states. This can help users better understand their minds and track their progress over time.The use of conductive fabric sensors and the Cortex M4 microcontroller enables real-time monitoring of brainwave frequencies and heart rate. This immediate feedback can assist users in adjusting their meditation techniques to achieve desired mental states more effectively.
The inclusion of an optical heart rate monitor and pulse oximeter adds another layer of physiological data to the meditation experience. This comprehensive data collection can provide users with insights into how their mental and physical states correlate. By employing microprocessors to analyze EEG signals across various frequency ranges, the device can extract valuable insights about different mental states, such as relaxation, focus, and stress. It's important to note that while the proposed model offers these advantages, there might also be challenges related to user comfort, data accuracy, and user acceptance. Regular updates, user feedback, and ongoing research can help address potential limitations and further refine the device's capabilities.
5 Claims & 1 Figure , Claims:The scope of the invention is defined by the following claims:
Claims:
1. The NeuroFeedback Meditation provides personalized meditation guidance based on real-time EEG data analysis comprising:
a) The users receive meditation recommendations tailored to their current mental states, leading to more effective and personalized meditation experiences.
b) The application objectively monitors users' mental states using EEG technology. It measures brainwave frequencies associated with emotions such as excitement, calmness, stress, and more, allowing users to gain insights into their mind's activities.
c) The EEG data analysis combines with meditation practices, NeuroFeedback Meditation helps users achieve a deeper state of relaxation. The application's tailored meditation sessions are designed to align with users' mental states, facilitating a more immersive meditation experience.
2. According to claim 1, the neurofeed meditation also helps the users with different head aches like migrain or sleeping disorder by suggesting related meditation activities. The initial calibration process, involving deep breathing and soft music, prepares users for meditation by helping them achieve a relaxed state of mind.
3. According to claim 1, the neuroFeedback Meditation ensures data privacy through a secure Bluetooth connection, maintaining the confidentiality of users' meditation-related data while facilitating seamless communication between the device and the application.
4. According to claim 1, the NeuroFeedback Meditation design allows to directly contact with the meditation trainers for the guidance as required by the user through a online meet.
5. According to claim 1, the EEG data analysis provides insights into different mental states, such as relaxation, focus, and stress. These insights can guide meditation practitioners in adapting their techniques for optimal outcomes.