1. A system (100) for hybrid eye mind-controlled interaction using a brain-computer interface, the system (100) comprising: an image acquisition unit (102) adapted to capture real-time facial and eye images of a user; an EEG acquisition unit (104) adapted to acquire electroencephalography signals from the user; and a processing unit (106) operatively coupled to the image acquisition unit (102) and the EEG acquisition unit (104), characterized in that the processing unit (106) is configured to: receive the captured facial and eye images from the image acquisition unit (102); receive the acquired electroencephalography signals from the EEG acquisition unit (104); detect a pupil position, a gaze direction, and fixation points from the received facial and eye images using a computer vision algorithm to determine a gaze target location on a graphical interface (110); extract cognitive state features selected from an attention level, a focus level, mental intention indicators, or a combination thereof from the received electroencephalography signals; identify an intentional activation signal based on the extracted cognitive state features; fuse the determined gaze target location with the identified intentional activation signal using a hybrid decision engine to verify user intent; generate a control command corresponding to the gaze target location, when the gaze target location and the intentional activation signal occur simultaneously; and execute the generated control command to perform hands-free interaction with a computing device (108).

2. The system (100) as claimed in claim 1, wherein the processing unit (106) is configured to detect gaze direction using facial landmark detection, head-pose estimation, and appearance-based gaze estimation models implemented through computer vision frameworks.

3. The system (100) as claimed in claim 1, wherein the processing unit (106) is configured to fuse the gaze target location and the intentional activation signal using an adaptive weighting algorithm or machine-learning-based fusion model to reduce false activations.

4. The system (100) as claimed in claim 1, wherein the processing unit (106) is configured to dynamically adjust decision thresholds based on a user attention level, environmental lighting conditions, an electroencephalography signals quality, or a combination thereof.

5. The system (100) as claimed in claim 1, wherein the image acquisition unit (102) is a webcam configured to capture the facial and eye images without infrared illumination.

6. The system (100) as claimed in claim 1, wherein the EEG acquisition unit (104) is a wearable headset adapted to be worn on a head of the user.

7. A method (300) for hybrid eye mind-controlled interaction using a brain-computer interface, the method (300) is characterized by the steps of: receiving captured facial and eye images from an image acquisition unit (102); receiving acquired electroencephalography signals from an EEG acquisition unit (104); detecting a pupil position, a gaze direction, and fixation points from the received facial and eye images using a computer vision algorithm to determine a gaze target location on a graphical interface (110); extracting cognitive state features selected from an attention level, a focus level, mental intention indicators, or a combination thereof from the received electroencephalography signals; identifying an intentional activation signal based on the extracted cognitive state features; fusing the determined gaze target location with the identified intentional activation signal using a hybrid decision engine to verify user intent; and generating a control command corresponding to the gaze target location, when the gaze target location and the intentional activation signal occur simultaneously.

8. The method (300) as claimed in claim 7, comprising a step of executing the generated control command to perform hands-free interaction with the computing device (108).

9. The method (300) as claimed in claim 7, wherein the image acquisition unit (102) is a webcam configured to capture the facial and eye images without infrared illumination.

10. The method (300) as claimed in claim 7, wherein the EEG acquisition unit (104) is a wearable headset adapted to be worn on a head of the user. Date: April 08, 2026 Place: Noida Nainsi Rastogi Patent Agent (IN/PA-2372) Agent for the Applicant

Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to human computer interaction technologies and particularly to a system and a method for hybrid eye mind-controlled interaction using brain-computer interface.
Description of Related Art
[002] Individuals with severe motor disabilities, paralysis, or limited mobility face major difficulty in interaction with computers, communication devices, and digital interfaces without physical input mechanisms. Conventional interaction techniques such as keyboard, mouse, or touch-based systems require reliable motor control, that cannot be possible for such users.
[003] Assistive technologies attempt to provide alternative interaction mechanisms; however, many solutions remain inaccessible, expensive, or technically complex for routine use. There exists a significant requirement for reliable and accessible human–computer interaction approaches that support hands-free operation for individuals with restricted physical movement.
[004] Several technologies exist for hands-free interaction and assistive communication. Eye-tracking systems constitute one category of such technologies and enable cursor navigation or interface control through gaze direction detection. Many commercial eye-tracking devices employ infrared illumination and specialized cameras for pupil detection and gaze mapping.
[005] Another category includes brain computer interface systems that interpret electroencephalography signals from a user in order to translate neural activity into control commands. Certain research systems also incorporate combinations of neural signals and eye-movement signals to enhance command detection reliability and user control.
[006] Despite the availability of such technologies, several limitations remain. Many eye-tracking systems require specialized infrared hardware, controlled lighting conditions, and expensive equipment, that restrict practical accessibility and widespread deployment. Brain computer interface systems that rely solely on electroencephalography signals often demonstrate limited spatial precision and slower command generation.
[007] Existing hybrid systems frequently depend on laboratory-grade sensors, complex calibration procedures, and specialized signal acquisition hardware. These constraints reduce portability, increase cost, and limit suitability for everyday assistive interaction environments.
[008] There is thus a need for an improved and advanced system and method for hybrid eye mind-controlled interaction using brain-computer interface that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[009] Embodiments in accordance with the present invention provide a system for hybrid eye mind-controlled interaction using brain-computer interface. The system comprising an image acquisition unit adapted to capture real-time facial and eye images of a user. The system further comprising an EEG acquisition unit adapted to acquire electroencephalography signals from the user. The system further comprising a processing unit operatively coupled to the image acquisition unit and the EEG acquisition unit, characterized in that the processing unit is configured to: receive the captured facial and eye images from the image acquisition unit; receive the acquired electroencephalography signals from the EEG acquisition unit; detect a pupil position, a gaze direction, and fixation points from the received facial and eye images using a computer vision algorithm to determine a gaze target location on a graphical interface; extract cognitive state features selected from an attention level, a focus level, mental intention indicators, or a combination thereof from the received electroencephalography signals; identify an intentional activation signal based on the extracted cognitive state features; fuse the determined gaze target location with the identified intentional activation signal using a hybrid decision engine to verify user intent; generate a control command corresponding to the gaze target location, when the gaze target location and the intentional activation signal occur simultaneously; and execute the generated control command to perform hands-free interaction with a computing device.
[0010] Embodiments in accordance with the present invention further provide a method for hybrid eye mind-controlled interaction using brain-computer interface. The method comprising steps of: receiving captured facial and eye images from an image acquisition unit, receiving acquired electroencephalography signals from an EEG acquisition unit, detecting a pupil position, a gaze direction, and fixation points from the received facial and eye images using a computer vision algorithm to determine a gaze target location on a graphical interface; extracting cognitive state features selected from an attention level, a focus level, mental intention indicators, or a combination thereof from the received electroencephalography signals; identifying an intentional activation signal based on the extracted cognitive state features; fusing the determined gaze target location with the identified intentional activation signal using a hybrid decision engine to verify user intent; generating a control command corresponding to the gaze target location, when the gaze target location and the intentional activation signal occur simultaneously and execute the generated control command to perform hands-free interaction with a computing device.
[0011] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for hybrid eye mind-controlled interaction using brain-computer interface.
[0012] Next, embodiments of the present application may provide a system for hybrid eye mind-controlled interaction using brain-computer interface that provides a low-cost hands-free interaction interface by utilizing consumer-grade hardware including a standard webcam and a portable electroencephalography headset.
[0013] Next, embodiments of the present application may provide a system for hybrid eye mind-controlled interaction using brain-computer interface that improves command reliability by integrating gaze direction detection with electroencephalography-based cognitive intention recognition.
[0014] Next, embodiments of the present application may provide a system for hybrid eye mind-controlled interaction using brain-computer interface that reduces false command activation through hybrid verification of user visual attention and neural intent signals.
[0015] Next, embodiments of the present application may provide a system for hybrid eye mind-controlled interaction using brain-computer interface that supports accessible computer interaction for individuals with motor disabilities or limited mobility without reliance on conventional physical input devices.
[0016] Next, embodiments of the present application may provide a system for hybrid eye mind-controlled interaction using brain-computer interface that enables portable and deployable assistive interaction in everyday environments without requirement of specialized infrared eye-tracking hardware or controlled laboratory conditions.
[0017] These and other advantages will be apparent from the present application of the embodiments described herein.
[0018] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0020] FIG. 1 illustrates a block diagram of a system for hybrid eye mind-controlled interaction using brain-computer interface, according to an embodiment of the present invention;
[0021] FIG. 2 illustrates components of a processing unit for the system hybrid eye mind-controlled interaction using brain-computer interface according to an embodiment of the present invention; and
[0022] FIG. 3 depicts a flowchart of a method for hybrid eye mind-controlled interaction using brain-computer interface, according to an embodiment of the present invention.
[0023] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0024] 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 scope of the invention as defined in the claims.
[0025] 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.
[0026] 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.
[0027] As used herein, the term “user” refers to a human subject or individual interacting with the system, wherein the user provides visual input through eye movement and neural input through electroencephalography signals for enabling hands-free interaction with a computing environment. The user may include, but not limited to, an individual with motor impairment, a physically able individual, or any person utilizing the system for human–computer interaction. Embodiments of the present invention are intended to include or otherwise cover any type of user, including known, related applications, and/or future use scenarios.
[0028] FIG. 1 illustrates a block diagram of a system 100 for hybrid eye mind-controlled interaction using brain computer interface, according to an embodiment of the present invention. The system 100 may provide a reliable and adaptive hands-free interaction mechanism that interprets visual attention and neural intention of a user to enable control of computing interfaces. The system 100 may facilitate real-time interaction with digital devices by combining gaze detection and electroencephalography signal interpretation for command generation. The system 100 may enable accessible and efficient hands-free interaction with computing systems through integrated analysis of the gaze detection information and the electroencephalography signals.
[0029] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise an image acquisition unit 102, an EEG acquisition unit 104, and a processing unit 106 operatively coupled to a computing device 108 and a graphical interface 110. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and the limitations of the existing systems.
[0030] In an embodiment of the present invention, the image acquisition unit 102 may be adapted to capture real-time facial and eye images of a user. The captured images may comprise visual parameters indicative of eye position, pupil location, and facial landmark positions. In an embodiment of the present invention, the image acquisition unit 102 may be a webcam configured to capture facial and eye images without use of infrared illumination. The gaze detection may be performed using visible spectrum image processing. The image acquisition unit 102 may include, but not limited to, a webcam, an integrated laptop camera, a mobile device camera, any imaging sensor and so forth capable of capturing facial and eye images. Embodiments of the present invention are intended to include or otherwise cover any type of the image acquisition unit 102 including known technologies, related art, and later developed imaging devices capable of capturing facial and eye images. The image acquisition unit 102 may be, but not limited to, a webcam, an integrated laptop camera, a mobile device camera, imaging sensors, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the image acquisition unit 102, including known, related art, and/or later developed technologies.
[0031] In an embodiment of the present invention, the EEG acquisition unit 104 may be adapted to acquire electroencephalography signals from the user. The acquired electroencephalography signals may comprise neural parameters indicative of cognitive states such as an attention level, a focus level, mental intention indicators, and so forth. The EEG acquisition unit 104 may include, but not limited to, portable EEG headsets, dry electrode EEG devices, wearable neural sensing devices, other brain-signal acquisition technologies, and so forth capable of detecting neural activity associated with cognitive states. Embodiments of the present invention are intended to include or otherwise cover any type of the EEG acquisition unit 104 including known technologies, related art, and later developed EEG sensing systems.
[0032] The EEG acquisition unit 104 may be, but not limited to, portable EEG headsets, dry electrode EEG devices, wearable neural sensing devices, wireless EEG systems, and so forth. In a preferred embodiment of the present invention, the EEG acquisition unit 104 may be a wearable headset adapted to be worn on a head of the user to acquire electroencephalography signals in real time. Embodiments of the present invention are intended to include or otherwise cover any type of the EEG acquisition unit 104, including known, related art, and/or later developed technologies.
[0033] In an embodiment of the present invention, the processing unit 106 may be operatively coupled to the image acquisition unit 102 and the EEG acquisition unit 104. The processing unit 106 may include, but not limited to, processors, memory modules, executable instructions and so forth. The processing unit 106 may receive captured facial and eye images from the image acquisition unit 102 and electroencephalography signals from the EEG acquisition unit 104 for further processing. The processing unit 106 may be configured to analyse the received facial and eye images using computer vision algorithms in order to detect pupil position, gaze direction, and fixation points. The detected visual parameters may assist the processing unit 106 in determining a gaze target location on the graphical interface 110 associated with the computing device 108. The computer vision algorithms may include, but are not limited to, facial landmark detection techniques, eye-region analysis methods, gaze estimation models, and other vision-based analytical techniques suitable for determining user gaze direction. Embodiments of the present invention are intended to include or otherwise cover any type of computer vision algorithms capable of determining gaze target location on the graphical interface 110.
[0034] In an embodiment of the present invention, the processing unit 106 may be configured to detect gaze direction using facial landmark detection, head-pose estimation, and appearance-based gaze estimation models implemented through computer vision frameworks. The facial landmark detection identifies eye-region features, the head-pose estimation compensates for user orientation, and the appearance-based gaze estimation models determine gaze direction from image data.
[0035] In an embodiment of the present invention, the computer vision algorithm used for gaze estimation may comprise appearance-based gaze estimation models implemented using machine learning techniques including convolutional neural networks, regression-based gaze mapping models, deep neural network architectures, and so forth. The appearance-based gaze estimation models may analyse eye region images, facial landmarks, and head-pose orientation to estimate gaze direction under varying lighting conditions and user head positions. The processing unit 106 may further implement head-pose compensation algorithms configured to correct gaze estimation errors caused by user head movement or camera misalignment. The head-pose compensation algorithms may analyse three-dimensional facial landmarks to estimate pitch, yaw, and roll orientation of the user head and adjust gaze direction estimation accordingly.
[0036] In an embodiment of the present invention, the processing unit 106 may be configured to implement an environmental robustness mechanism to compensate for variations in ambient lighting conditions, user head pose, and distance between the image acquisition unit 102 and the user. The processing unit 106 may dynamically adjust gaze estimation parameters and apply normalization techniques to maintain consistent performance across varying environmental conditions.
[0037] In an embodiment of the present invention, the processing unit 106 may further analyse the received electroencephalography signals to extract cognitive state features. The extracted cognitive state features may comprise parameters indicative of the attention level, the focus level, mental intention indicators, and so forth. The preprocessing operations may be performed on the electroencephalography signals to improve signal quality prior to feature extraction. The preprocessing operations may include, but not limited to noise filtering, artifact removal, signal normalization, signal conditioning techniques, a and other signal processing techniques suitable for electroencephalography signal conditioning. Based on the extracted cognitive state features, the processing unit 106 may identify an intentional activation signal that represents a deliberate user intention to activate a command associated with the gaze target location. The intentional activation signal may correspond to neural indicators that confirm that the user intends to perform an interaction with the graphical interface 110.
[0038] In an embodiment of the present invention, the cognitive state features extracted from the electroencephalography signals may be derived using spectral analysis techniques, the techniques including power spectral density estimation, frequency band analysis, neural signal classification models, and so forth. The processing unit 106 may analyse variations in frequency bands including alpha, beta, gamma bands, or combination thereof to determine attention level, focus level, and mental intention indicators of the user.
[0039] In an embodiment of the present invention, the processing unit 106 may be configured to identify the intentional activation signal based on predefined electroencephalography signal patterns including an increase in beta frequency band power, a computed focus index exceeding a threshold value, or a variation in attention-related neural indicators. The processing unit 106 may be configured to dynamically determine threshold values based on user-specific signal characteristics.
[0040] In an embodiment of the present invention, the processing unit 106 may be configured to perform real-time continuous fusion of gaze data and electroencephalography signals by synchronizing data streams received from the image acquisition unit 102 and the EEG acquisition unit 104. The processing unit 106 may continuously evaluate temporal alignment between gaze fixation events and intentional activation signals to ensure real-time responsiveness.
[0041] In an embodiment of the present invention, the adaptive calibration mechanism may be configured to continuously update system parameters based on user fatigue indicators, electroencephalography signal quality metrics, environmental lighting variations, and gaze estimation accuracy. The processing unit 106 may implement machine learning-based adaptation techniques to refine calibration parameters without requiring explicit user intervention.
[0042] In an embodiment of the present invention, the system 100 may be configured to minimize or eliminate user-specific calibration procedures by employing generalized machine learning models and adaptive thresholding techniques within the processing unit 106. The system 100 may enable immediate usability with minimal setup requirements.
[0043] In an embodiment of the present invention, the processing unit 106 may further fuse the determined gaze target location with the identified intentional activation signal through a hybrid decision engine. The hybrid decision engine may verify user intent by confirming simultaneous occurrence of gaze fixation and neural activation indicators. The hybrid decision process may reduce false command activation that may arise from unintentional eye movements or non-intentional neural fluctuations. Upon verification of user intent, the processing unit 106 may generate a control command corresponding to the gaze target location identified on the graphical interface 110. The generated control command may include, but not limited to, cursor selection, interface navigation, icon activation, menu selection, control operations, and so forth associated with the computing device 108.
[0044] In an embodiment of the present invention, the processing unit 106 may be configured to fuse the gaze target location and the intentional activation signal using an adaptive weighting algorithm or a machine-learning-based fusion model to reduce false activations by dynamically assigning weights to gaze input and electroencephalography signal input.
[0045] In an embodiment of the present invention, the processing unit 106 may be configured to implement a dual-stage intent confirmation mechanism. The gaze target location determined from the image acquisition unit 102 represents a selection input and the intentional activation signal derived from the EEG acquisition unit 104 represents a confirmation input. The processing unit 106 may be configured to execute the control command only when the confirmation input validates the selection input, thereby ensuring intentional user interaction.
[0046] In an embodiment of the present invention, the processing unit 106 may be configured to define a temporal synchronization window within which the gaze target location and the intentional activation signal must occur to validate user intent. The temporal synchronization window may be dynamically adjusted based on user response time and latency of the system 100.
[0047] In an embodiment of the present invention, the processing unit 106 may compute an attention index or focus index derived from electroencephalography signal features. The attention index may represent the level of the user cognitive engagement and may be used by the hybrid decision engine to confirm deliberate interaction with the graphical interface 110. In an embodiment of the present invention, the processing unit 106 may be configured to prevent false command activation by enforcing a dual-condition execution rule. The control command may be generated only when both the gaze target location and the intentional activation signal satisfy predefined temporal and threshold conditions.
[0048] In an embodiment of the present invention, the hybrid decision engine may further implement an adaptive calibration mechanism configured to dynamically adjust decision thresholds associated with gaze fixation detection and intentional activation signals. The adaptive calibration mechanism may analyse parameters. The parameters may be, but not limited to, a user attention level, an electroencephalography signal quality, environmental lighting conditions, a user fatigue indicators, and so forth. Based on the analysed parameters, the hybrid decision engine may automatically update activation thresholds and fusion weights to maintain reliable command detection during prolonged usage or varying environmental conditions. In an embodiment of the present invention, the processing unit 106 may be configured to dynamically adjust decision thresholds based the parameters to maintain consistent command detection accuracy.
[0049] In an embodiment of the present invention, the generated control command may be transmitted to the computing device 108 for execution. Execution of the control command may enable interaction with the graphical interface 110 without requirement of conventional input devices such as a keyboard, mouse, touch interface, and combination thereof. In an embodiment of the present invention, the system 100 may be specifically configured to operate using consumer-grade hardware components including the image acquisition unit 102 and the EEG acquisition unit 104, thereby eliminating dependency on specialized imaging systems and high-cost laboratory equipment.
[0050] The processing unit 106 may be, but not limited to, microprocessors, central processing units, graphics processing units, embedded processors, memory-integrated processors, or cloud-based processing systems, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processing unit 106, including known, related art, and/or later developed technologies. The processing unit 106 may further be explained in detail in conjunction with FIG. 2.
[0051] In an embodiment of the present invention, the system 100 may be configured based on a cost-optimized architecture. The image acquisition unit 102 and the EEG acquisition unit 104 comprise consumer-grade hardware components to reduce costing of the system 100 while maintaining functional accuracy. The processing unit 106 may be configured to compensate for hardware limitations through software-based enhancement techniques including algorithmic correction, signal normalization, and adaptive learning mechanisms. The cost-optimized architecture may eliminate requirement of specialized infrared imaging devices and high-end laboratory-grade electroencephalography systems.
[0052] In an embodiment of the present invention, the computing device 108 may be operatively coupled to the processing unit 106 and configured to receive control commands generated by the processing unit 106 for execution. The computing device 108 may enable hands-free interaction by executing the control commands to control a graphical interface 110.
[0053] In an embodiment of the present invention, the computing device 108 may be configured to execute application-level interactions including assistive communication interfaces, smart home device control, human–computer interaction systems, and cursor-based navigation systems. The control commands generated by the processing unit 106 may be mapped to application-specific actions.
[0054] In an embodiment of the present invention, the computing device 108 may be configured to provide an application programming interface enabling integration of the system 100 with external software platforms, assistive technologies, or Internet-of-Things devices. The application programming interface may allow external systems to receive control commands and system feedback.
[0055] In an embodiment of the present invention, the computing device 108 may include, but not limited to, a desktop computer, a laptop computer, a tablet device, a mobile device, an embedded computing system, or any other programmable electronic device capable of executing instructions. Embodiments of the present invention are intended to include or otherwise cover any type of the computing device 108, including known, related art, and/or later developed technologies.
[0056] In an embodiment of the present invention, the computing device 108 may comprise hardware components including a processor, a memory unit, input-output interfaces, and communication interfaces. The computing device 108 may be configured to process received control commands and perform corresponding operations on the graphical interface 110.
[0057] In an embodiment of the present invention, the computing device 108 may be configured to communicate with the processing unit 106 through wired communication interfaces, wireless communication interfaces, or network-based communication protocols.
[0058] In an embodiment of the present invention, the computing device 108 may be configured to support assistive interaction applications, smart home control systems, communication aids, or human–computer interaction platforms. In an embodiment of the present invention, the computing device 108 may be configured to provide real-time feedback through the graphical interface 110 in response to execution of the control commands. The feedback may include visual indicators, cursor highlighting, or confirmation signals.
[0059] In an embodiment of the present invention, the computing device 108 may be configured to operate in conjunction with consumer-grade hardware without requirement of specialized infrared-based eye-tracking systems or high-end laboratory equipment.
[0060] In an embodiment of the present invention, the graphical interface 110 may provide real-time visual feedback to the user in response to detected gaze and intentional activation signals. The visual feedback may include, but not limited to, cursor highlighting, visual confirmation indicators, target highlighting, activation icons, and so forth to inform the user that the command has been successfully recognized by the system. The processing unit 106 may dynamically adjust cursor movement speed, dwell time thresholds, command activation delay based on the detected attention level of the user derived from electroencephalography signals. The graphical interface 110 may be, but not limited to, graphical user interfaces, display interfaces, application interfaces, assistive communication interfaces, or other visual interaction platforms, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the graphical interface 110, including known, related art, and/or later developed technologies.
[0061] FIG. 2 illustrates components of the processing unit 106 of the system 100 for hybrid eye mind-controlled interaction, according to an embodiment of the present invention. The processing unit 106 may comprise a gaze detection module 200, a signal processing module 202, an intent detection module 204, and a commanding module 206.
[0062] In an embodiment of the present invention, the gaze detection module 200 may be configured to receive facial and eye images from the image acquisition unit 102. The gaze detection module 200 may be configured to process the received facial and eye images to detect a pupil position, a gaze direction, a fixation point, and so forth, of the user.
[0063] In an embodiment of the present invention, the gaze detection module 200 may utilize computer vision algorithms to analyse facial landmarks and eye regions in the received facial and eye images. Further, the gaze detection module 200 may be configured to determine a gaze target location on the graphical interface 110 based on the detected pupil position, gaze direction, and fixation points, or the combination thereof. The gaze detection module 200 may be configured to transmit the determined gaze target location to the commanding module 206.
[0064] In an embodiment of the present invention, the signal processing module 202 may be configured to receive electroencephalography signals from the EEG acquisition unit 104. The signal processing module 202 may be further configured to process the received electroencephalography signals to improve signal quality for further analysis. The signal processing module 202 may be configured to perform preprocessing operations on the received electroencephalography signals. The preprocessing operations may include, but not limited to, noise filtering, artifact removal, signal normalization, and so forth. The signal processing module 202 may be configured to generate conditioned electroencephalography signal data suitable for extraction of cognitive state features. The signal processing module 202 may be configured to transmit the conditioned electroencephalography signal data to the intent detection module 204.
[0065] In an embodiment of the present invention, the intent detection module 204 may be configured to receive the conditioned electroencephalography signal data from the signal processing module 202. The intent detection module 204 may be configured to analyse the conditioned electroencephalography signal data to extract cognitive state features corresponding to the attention level, the focus level, mental intention indicators, or the combination thereof. The intent detection module 204 may be configured to identify an intentional activation signal based on the extracted cognitive state features. The intentional activation signal may indicate a deliberate intention of the user to activate a command associated with the gaze target location. Further, the intent detection module 204 may be configured to transmit the identified intentional activation signal to the commanding module 206.
[0066] In an embodiment of the present invention, the commanding module 206 may be configured to receive the determined gaze target location from the gaze detection module 200 and the intentional activation signal from the intent detection module 204. The commanding module 206 may be configured to fuse the determined gaze target location with the identified intentional activation signal to verify user intent. If the commanding module 206 determines that the gaze target location and the intentional activation signal occur simultaneously, then the commanding module 206 may be configured to generate the control command corresponding to the gaze target location. Further, the commanding module 206 may be configured to transmit the generated control command to the computing device 108 for execution. The executed control command may enable hands-free interaction with the graphical interface 110 associated with the computing device 108.
[0067] FIG. 3 depicts a flowchart of a method 300 for the hybrid eye mind-controlled interaction using the brain-computer interface, according to an embodiment of the present invention.
[0068] At step 302, the system 100 may receive the captured facial and eye images from the image acquisition unit 102.
[0069] At step 304, the system 100 may receive the acquired electroencephalography signals from the EEG acquisition unit 104.
[0070] At step 306, the system 100 may detect the pupil position, the gaze direction, fixation points, and so forth, from the received facial and eye images using the computer vision algorithm to determine the gaze target location on the graphical interface 110.
[0071] At step 308, the system 100 may extract the cognitive state features selected from the attention level, the focus level, the mental intention indicators, and so forth from the received electroencephalography signals.
[0072] At step 310, the system 100 may identify the intentional activation signal based on the extracted cognitive state features.
[0073] At step 312, the system 100 may fuse the determined gaze target location with the identified intentional activation signal using the hybrid decision engine to verify user intent.
[0074] At step 314, the system 100 may generate the control command corresponding to the gaze target location, when the gaze target location and the intentional activation signal occur simultaneously.
[0075] At step 316, the system 100 may, execute the generated control command to perform hands-free interaction with the computing device 108.
[0076] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0077] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A system (100) for hybrid eye mind-controlled interaction using a brain-computer interface, the system (100) comprising:
an image acquisition unit (102) adapted to capture real-time facial and eye images of a user;
an EEG acquisition unit (104) adapted to acquire electroencephalography signals from the user; and
a processing unit (106) operatively coupled to the image acquisition unit (102) and the EEG acquisition unit (104), characterized in that the processing unit (106) is configured to:
receive the captured facial and eye images from the image acquisition unit (102);
receive the acquired electroencephalography signals from the EEG acquisition unit (104);
detect a pupil position, a gaze direction, and fixation points from the received facial and eye images using a computer vision algorithm to determine a gaze target location on a graphical interface (110);
extract cognitive state features selected from an attention level, a focus level, mental intention indicators, or a combination thereof from the received electroencephalography signals;
identify an intentional activation signal based on the extracted cognitive state features;
fuse the determined gaze target location with the identified intentional activation signal using a hybrid decision engine to verify user intent;
generate a control command corresponding to the gaze target location, when the gaze target location and the intentional activation signal occur simultaneously; and
execute the generated control command to perform hands-free interaction with a computing device (108).
2. The system (100) as claimed in claim 1, wherein the processing unit (106) is configured to detect gaze direction using facial landmark detection, head-pose estimation, and appearance-based gaze estimation models implemented through computer vision frameworks.
3. The system (100) as claimed in claim 1, wherein the processing unit (106) is configured to fuse the gaze target location and the intentional activation signal using an adaptive weighting algorithm or machine-learning-based fusion model to reduce false activations.
4. The system (100) as claimed in claim 1, wherein the processing unit (106) is configured to dynamically adjust decision thresholds based on a user attention level, environmental lighting conditions, an electroencephalography signals quality, or a combination thereof.
5. The system (100) as claimed in claim 1, wherein the image acquisition unit (102) is a webcam configured to capture the facial and eye images without infrared illumination.
6. The system (100) as claimed in claim 1, wherein the EEG acquisition unit (104) is a wearable headset adapted to be worn on a head of the user.
7. A method (300) for hybrid eye mind-controlled interaction using a brain-computer interface, the method (300) is characterized by the steps of:
receiving captured facial and eye images from an image acquisition unit (102);
receiving acquired electroencephalography signals from an EEG acquisition unit (104);
detecting a pupil position, a gaze direction, and fixation points from the received facial and eye images using a computer vision algorithm to determine a gaze target location on a graphical interface (110);
extracting cognitive state features selected from an attention level, a focus level, mental intention indicators, or a combination thereof from the received electroencephalography signals;
identifying an intentional activation signal based on the extracted cognitive state features;
fusing the determined gaze target location with the identified intentional activation signal using a hybrid decision engine to verify user intent; and
generating a control command corresponding to the gaze target location, when the gaze target location and the intentional activation signal occur simultaneously.
8. The method (300) as claimed in claim 7, comprising a step of executing the generated control command to perform hands-free interaction with the computing device (108).
9. The method (300) as claimed in claim 7, wherein the image acquisition unit (102) is a webcam configured to capture the facial and eye images without infrared illumination.
10. The method (300) as claimed in claim 7, wherein the EEG acquisition unit (104) is a wearable headset adapted to be worn on a head of the user.
Date: April 08, 2026
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant