Description:FIELD OF THE INVENTION

[0001] The present invention relates to a brain-computer Interface (BCI) system for enhancing smartphone accessibility for individuals with motor impairments that is capable of allowing individuals with motor impairments to control smartphone functions using brainwave signals and improving the accuracy of command recognition through continuous learning based on user interaction, thereby making daily tasks easier and more accessible.

BACKGROUND OF THE INVENTION

[0002] The use of smartphones has become an essential part of daily life, enabling communication, information access, and control of various digital services. For most users, interacting with a smartphone is straightforward, involving touch gestures, voice commands, or external accessories. However, individuals with motor impairments often face significant challenges when using these standard input methods. As a result, there is a growing need for systems that provide alternative, accessible, and efficient ways for these users to interact with smartphones and related devices.

[0003] Traditionally, people with motor impairments rely on assistive technologies such as external switches, eye-tracking systems, or voice recognition tools to operate smartphones and computers. While these tools have improved access, they often require additional hardware, precise calibration, or a controlled environment to function effectively. Furthermore, many of these systems be slow, lack adaptability, or are not compatible with all types of applications and devices. This limits the overall user experience and independence of individuals who depend on assistive solutions.

[0004] In addition, traditional methods may not offer real-time feedback or personalized learning capabilities. They generally follow fixed interaction patterns that may not suit every user's unique needs or preferences. This lack of customization result in low accuracy, frustration, and reduced usability over time. Security and privacy in these systems are also concerns, as they may not offer adequate protection for the sensitive data being transmitted or processed. These limitations highlight the need for more adaptive, secure, and user-friendly solutions for smartphone accessibility.

[0005] US10019060B2 discloses about Generally described, the present application relates to a system and method for processing input to control a set of functions and features. More specifically, however, the present application relates to user devices that can be controlled through brain activities or similar actions. In an illustrative embodiment, brain activities are monitored through electroencephalography (EEG). Through EEG, input waves that can be appropriately monitored can be sent to a virtual assistant. The virtual assistant can decipher the number of signals coming and determine a correct output such as a function or feature to be manipulated. In the implementation presented herein, features and functions of a smartphone can be manipulated. Other types of user devices can also be controlled, such as in-vehicle systems, head units, televisions, tablets, computer, laptops, etc.

[0006] US20150338917A1 discloses a method of controlling an electronic device thought, includes: capturing through one or more electrodes, located in proximity to a brain of a user, signals of brainwave activity of said user; analyzing said signals to detect a pattern of brainwave activity of said user; based on the detected pattern, determining that the user thinks about a command that controls an electronic device; and based on said determining, triggering the electronic device to perform said command.

[0007] Conventionally, many system are available for enhancing smartphone accessibility for individuals with motor impairments. However, the cited inventions show certain limitation where the system lacks adaptability, real-time feedback, personalization, and robust security. They often require additional hardware, are slow, and lack compatibility with all devices, reducing efficiency and user independence.

[0008] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system that offers adaptive, secure, and user-friendly smartphone control. Such that the system should provide real-time feedback, personalized interaction, and seamless integration with various devices—without relying on complex hardware setups thereby enhancing accessibility and independence for users with motor impairments.

OBJECTS OF THE INVENTION

[0009] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0010] An object of the present invention is to develop a system that that allows individuals with motor impairments to control smartphone functions using brainwave signals, thereby improving the accuracy of command recognition through continuous learning based on user interaction.

[0011] Another object of the present invention is to develop a system that is capable of enable real-time execution of user commands on smartphones with minimal delay, thereby natural user experience.

[0012] Yet another object of the present invention is to develop a system that is capable of ensuring securing and private handling of user data and transmitted commands during system operation, thereby protecting the user’s privacy.

[0013] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0014] The present invention relates to a brain-computer Interface (BCI) system for enhancing smartphone accessibility for individuals with motor impairments that is capable of ensuring secure and private handling of user data and transmitted commands during system operation and enabling real-time execution of user commands on smartphones with minimal delay, thereby helping the system to perform more reliably with repeated use.

[0015] According to an embodiment of the present invention, a brain-computer Interface (BCI) system for enhancing smartphone accessibility for individuals with motor impairments comprising a brain activity detection module, comprising a non-invasive electroencephalography (EEG) headset, configured to detect and capture brainwave signals generated by a user's neural activity, wherein the module is worn on the user's head to acquire electrical signals, signal processing module, communicatively coupled to the brain activity detection module, configured to receive the brainwave signals, process and decode the signals into executable commands using protocols, and ensure real-time processing with minimal latency, a command transmission module, communicatively coupled to the signal processing module, configured to wirelessly transmit the decoded commands to a computing unit via a communication module such as Bluetooth or Wi-Fi, a smartphone interaction module, integrated within the computing unit and communicatively coupled to the command transmission module, configured to receive the transmitted commands and execute corresponding actions on the computing unit, including typing, swiping, navigating applications, or activating accessibility features, a feedback and adaptive learning module, communicatively coupled to the computing unit interaction module and the signal processing module, configured to provide real-time feedback to the user to confirm executed actions and adaptively learn from user interactions to improve command accuracy over time.

[0016] According to another embodiment of the present invention, the system discloses herein the brain activity detection module further includes a noise filtering component configured to preprocess brainwave signals to remove artifacts and enhance signal clarity before transmission to the signal processing module, the signal processing module further configured with machine learning protocols, configured to adaptively map brainwave patterns to specific computing unit’s commands based on user-specific neural activity, improving command interpretation accuracy over time, smartphone interaction module, further comprising a privacy and security component configured to encrypt transmitted commands and user data, ensuring secure communication between the command transmission module and the computing unit, and providing user-controlled data management options, feedback and adaptive learning module, wherein this module is further configured to provide customizable feedback settings, allowing users to select preferred feedback modalities, such as visual, auditory, or haptic, based on their specific needs and the smartphone interaction module is further configured to integrate with existing mobile operating systems, including Android and iOS, to enable compatibility with standard smartphone applications and home system control via brainwave commands.

[0017] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates a flow chart depicting a brain-computer Interface (BCI) system for enhancing smartphone accessibility for individuals with motor impairments.

DETAILED DESCRIPTION OF THE INVENTION

[0019] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0020] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0021] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0022] The present invention relates to a brain-computer Interface (BCI) system for enhancing smartphone accessibility for individuals with motor impairments that is capable of allowing individuals with motor impairments to control smartphone functions using brainwave signals and improving the accuracy of command recognition through continuous learning based on user interaction, thereby enhancing natural user experience.

[0023] Referring to Figure 1, a flow chart depicting workflow of a brain-computer Interface (BCI) system for enhancing smartphone accessibility for individuals with motor impairments. The system discloses herein a brain activity detection module, comprising a non-invasive electroencephalography (EEG) headset, configured to detect and capture brainwave signals generated by a user's neural activity, wherein the module is worn on the user's head to acquire electrical signals. The brain activity detection module further includes a noise filtering component configured to preprocess brainwave signals to remove artifacts and enhance signal clarity before transmission to the signal processing module.

[0024] The brain activity detection module is a system designed to monitor and interpret electrical signals produced by the brain. It typically uses sensors like electroencephalography (EEG) electrodes placed on the scalp to detect neural activity. The module processes these signals using filters and protocols to remove noise and extract meaningful patterns. The module used to assess mental states, detect neurological disorders, or control external devices in brain-computer interfaces (BCIs). Modern modules often integrate wireless data transmission and real-time analytics, making them suitable for both clinical and consumer applications such as neurofeedback, cognitive training.

[0025] The brain activity detection module comprises non-invasive electroencephalography (EEG) headset designed to detect and capture brainwave signals generated by the user’s neural activity. The headset is worn on the user's head, where multiple electrodes typically made from conductive materials like silver chloride are positioned on the scalp to acquire electrical signals produced by neurons firing in the brain. These signals are extremely weak, typically in the range of microvolts, and require amplification.

[0026] The module includes preamplifiers and analog-to-digital converters to process the signals, which are then filtered to remove noise and artifacts. Data is transmitted to a processing unit for real-time analysis. The headset allows for continuous, non-invasive brain monitoring without penetrating the skull, enabling applications in healthcare, neuro feedback, and brain-computer interfaces.

[0027] The brain activity detection module includes the noise filtering component designed to preprocess raw brainwave signals captured by the EEG headset. This component removes unwanted artifacts such as muscle movements, eye blinks, and electrical interference that distort the neural signals. It uses digital signal processing techniques like band-pass filtering, notch filtering, and independent component analysis (ICA) to isolate true brain activity. By enhancing signal clarity, the noise filtering component ensures that only clean, reliable data is forwarded to the signal processing module. This improves the accuracy and efficiency of subsequent analysis in applications such as cognitive monitoring and brain-computer interfacing.

[0028] Post acquiring the electrical signals a signal processing module, communicatively coupled to the brain activity detection module, configured to receive the brainwave signals, process and decode the signals into executable commands using protocol and ensure real-time processing with minimal latency. The signal processing module further configured with machine learning protocols, configured to adaptively map brainwave patterns to specific computing unit’s commands based on user-specific neural activity, improving command interpretation accuracy over time.

[0029] The signal processing module is a critical component of the brain activity detection system, responsible for analyzing the preprocessed brainwave signals received from the noise filtering component. It utilizes protocols to interpret neural data, extracting meaningful patterns related to brain states, cognitive functions, or emotional responses. Techniques such as Fast Fourier Transform (FFT), machine learning, and time-frequency analysis are commonly employed to classify and quantify different brainwave types (e.g., alpha, beta, delta, theta). The processed information then be used for various applications, including neurofeedback, mental health assessment, or controlling external devices in brain-computer interface systems, enabling real-time interaction and feedback.

[0030] The signal processing module, communicatively coupled to the brain activity detection module, is designed to receive brainwave signals and transform them into executable commands. It employs protocols such as machine learning, pattern recognition, and signal decomposition to decode neural activity into actionable outputs. This module ensures real-time processing with minimal latency, enabling immediate system response to user intent. Key techniques include Fast Fourier Transform (FFT) for frequency analysis and adaptive filtering for precision. By translating complex brainwave patterns into digital commands, the module supports seamless integration with external devices in applications like brain-computer interfaces, assistive technology, and cognitive state monitoring.

[0031] Upon processing the received brainwave signals a command transmission module, communicatively coupled to the signal processing module, configured to wirelessly transmit the decoded commands to a computing unit via a communication module such as Bluetooth or Wi-Fi.

[0032] The command transmission module, communicatively coupled to the signal processing module, is responsible for wirelessly transmitting decoded brainwave-based commands to an external computing unit. The module integrates communication interface, such as Bluetooth, Wi-Fi, or other wireless protocols, to ensure secure and efficient data transfer.

[0033] This module encodes the processed commands into standardized data packets and manages synchronization and error-checking to maintain signal integrity during transmission. Low-latency and high-throughput communication protocols are utilized to ensure real-time responsiveness. The module is optimized for low power consumption and seamless pairing with various devices, making it suitable for applications in, brain-computer interfaces, and remote monitoring.

[0034] Further a smartphone interaction module, integrated within the computing unit and communicatively coupled to the command transmission module, configured to receive the transmitted commands and execute corresponding actions on the computing unit, including typing, swiping, navigating applications, or activating accessibility features. The smartphone interaction module, further comprising a privacy and security component configured to encrypt transmitted commands and user data, ensuring secure communication between the command transmission module and the computing unit, and providing user-controlled data management options.

[0035] The smartphone interaction module enables seamless communication between a brain activity detection system and a smartphone. It receives decoded commands wirelessly via Bluetooth, Wi-Fi, or similar protocols and translates them into actions on the smartphone, such as app control, text input, or device navigation. The module integrates with the smartphone’s operating system through APIs, allowing real-time interaction with applications. It supports low-latency data exchange and ensures secure, reliable connectivity.

[0036] This module enhances user experience by enabling hands-free control, accessibility features, and personalized interfaces, making it essential for brain-computer interfaces. The smartphone interaction module is further configured to integrate with existing mobile operating systems, including Android and iOS, to enable compatibility with standard smartphone applications and home system control via brainwave commands.

[0037] The smartphone interaction module is a component integrated within the computing unit and connected to the command transmission module. Its primary role is to receive commands transmitted from external sources, such as gesture or voice-based interfaces. Once these commands are received, the module interprets and executes them as actions on the smartphone. These actions include typing text, swiping across the screen, navigating through various applications, or activating built-in accessibility features.

[0038] The smartphone interaction module includes a privacy and security component designed to protect user data and transmitted commands. This component encrypts all communication between the command transmission module and the computing unit, ensuring secure and tamper-proof data exchange. It safeguards sensitive information from unauthorized access or interception during transmission.

[0039] Additionally, it provides user-controlled data management options, allowing users to monitor, restrict, or delete stored data as needed. This enhances user privacy and aligns with data protection standards, making the system trustworthy and secure for handling personal information during interaction with smartphone features and applications.

[0040] The smartphone interaction module is designed for integration with major mobile operating systems. This compatibility ensures that it interact with standard smartphone applications without requiring modifications to the protocol. By translating brainwave commands into recognizable input actions, the module enables users to control various smartphone functions and home systems, such as lighting, thermostats, or security devices. This integration supports intuitive, hands-free operation, enhancing accessibility and convenience. It bridges neural input technology with everyday digital environments, providing a unified interface for managing both mobile applications and connected home ecosystems.

[0041] To a feedback and adaptive learning module, communicatively coupled to the computing unit interaction module and the signal processing module, configured to provide real-time feedback to the user to confirm executed actions and adaptively learn from user interactions to improve command accuracy over time. The feedback and adaptive learning module, this module is further configured to provide customizable feedback settings, allowing users to select preferred feedback modalities, such as visual, auditory, or haptic, based on their specific needs.

[0042] The feedback and adaptive learning module is designed to enhance system performance through continuous user interaction analysis. It monitors user behavior, preferences, and responses to issued commands, collecting data to evaluate the effectiveness and accuracy of system actions. Using this feedback, the module employs machine learning protocols to adapt and personalize future interactions, improving responsiveness and usability over time. It adjust command recognition patterns, suggest optimizations, and refine control mechanisms based on individual user needs. This dynamic adaptation ensures a more intuitive and efficient user experience, particularly beneficial for users with specific interaction styles or accessibility requirements.

[0043] The present invention, works best in the following manner, where the brain activity detection module, comprising the non-invasive electroencephalography (EEG) headset worn on the user's head, which detects and captures brainwave signals generated by the user's neural activity. These signals are preprocessed by the noise filtering component to remove artifacts and enhance clarity before being transmitted to the signal processing module. The signal processing module, equipped with machine learning protocols, receives the brainwave signals, processes and decodes them into executable commands using adaptive mapping based on user-specific neural activity, ensuring real-time processing with minimal latency. The decoded commands are then wirelessly transmitted via the command transmission module using communication technologies such as Bluetooth or Wi-Fi to thecomputing unit. The smartphone interaction module, integrated within the computing unit, receives these commands and executes corresponding actions such as typing, swiping, application navigation, or activating accessibility features. This module also includes the privacy and security component to encrypt commands and user data, ensuring secure communication and allowing user-controlled data management. The smartphone interaction module is designed to integrate with Android and iOS operating systems for compatibility with standard applications and home automation systems. Finally, the feedback and adaptive learning module provides real-time feedback—customizable as visual, auditory, or haptic—to confirm executed actions, and continuously learns from user interactions to enhance command accuracy over time.

[0044] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A brain-computer Interface (BCI) system for enhancing smartphone accessibility for individuals with motor impairments, comprising:

i) a brain activity detection module, comprising a non-invasive electroencephalography (EEG) headset, configured to detect and capture brainwave signals generated by a user's neural activity, wherein the module is worn on the user's head to acquire electrical signals;
ii) a signal processing module, communicatively coupled to the brain activity detection module, configured to receive the brainwave signals, process and decode the signals into executable commands using protocols, and ensure real-time processing with minimal latency;
iii) a command transmission module, communicatively coupled to the signal processing module, configured to wirelessly transmit the decoded commands to a computing unit via a communication module such as Bluetooth or Wi-Fi;
iv) a smartphone interaction module, integrated within the computing unit and communicatively coupled to the command transmission module, configured to receive the transmitted commands and execute corresponding actions on the computing unit, including typing, swiping, navigating applications, or activating accessibility features; and
v) a feedback and adaptive learning module, communicatively coupled to the computing unit interaction module and the signal processing module, configured to provide real-time feedback to the user to confirm executed actions and adaptively learn from user interactions to improve command accuracy over time.

2) The system as claimed in claim 1, the brain activity detection module further includes a noise filtering component configured to preprocess brainwave signals to remove artifacts and enhance signal clarity before transmission to the signal processing module.

3) The system as claimed in claim 1, wherein the signal processing module further configured with machine learning protocols, configured to adaptively map brainwave patterns to specific computing unit’s commands based on user-specific neural activity, improving command interpretation accuracy over time.

4) The system as claimed in claim 1, wherein smartphone interaction module, further comprising a privacy and security component configured to encrypt transmitted commands and user data, ensuring secure communication between the command transmission module and the computing unit, and providing user-controlled data management options.

5) The system as claimed in claim 1, wherein feedback and adaptive learning module, wherein this module is further configured to provide customizable feedback settings, allowing users to select preferred feedback modalities, such as visual, auditory, or haptic, based on their specific needs.

6) The system as claimed in claim 1, wherein the smartphone interaction module is further configured to integrate with existing mobile operating systems, including Android and iOS, to enable compatibility with standard smartphone applications and home system control via brainwave commands.