Description:Field of the Invention

The present disclosure generally relates to user authentication systems. Particularly, the present disclosure relates to a system for authenticating a user based on brainwave signals.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
In the evolving landscape of information security, the verification of user identity has become paramount. Traditional authentication mechanisms, including passwords, PINs, and biometric systems, serve as the primary means to secure access to information systems and data. However, the limitations and vulnerabilities inherent in these traditional systems have led to an increased interest in more advanced and secure methods of authentication. Among these, biometric authentication methods, which rely on the unique physical or behavioral characteristics of individuals, have gained prominence. Yet, even within the realm of biometric authentication, issues such as falsification, replication of biometric traits, and privacy concerns persist.
A significant advancement in the field of biometric authentication is the exploration of neurobiological signals for user identification. Specifically, electroencephalogram (EEG) signals, which represent the brain's electrical activity, have been identified as a potent source for authentication purposes. EEG-based authentication systems leverage the unique patterns of brainwave signals generated by individuals in response to specific stimuli or at rest. The premise of using EEG signals for authentication lies in the inherent uniqueness of these signals among individuals, making them extremely difficult to replicate or forge.
EEG-based authentication involves the collection of brainwave signals through non-invasive methods, typically employing EEG headsets. These devices are designed to detect the electrical activity of the brain and output the corresponding EEG signal sources for further processing. The process of authenticating a user based on EEG signals encompasses several steps, beginning with the acquisition of EEG data. This step is crucial, as the quality and reliability of the EEG signals directly influence the system's effectiveness. Following acquisition, the EEG signals often undergo a series of enhancement procedures aimed at amplifying and filtering the raw signals to isolate the most relevant features for authentication.
Feature extraction from the enhanced EEG signals is a critical step, involving the identification and isolation of characteristic features that can be used to distinguish between individual users reliably. This process is dependent on sophisticated algorithms capable of analyzing the complex patterns within EEG signals to extract meaningful features. The extracted features are then compared against pre-stored profiles within a user database as part of the authentication process. The authentication algorithm evaluates the similarity between the incoming features and the stored profiles to determine the identity of the user. Successful authentication occurs when a match is found, thereby granting the user access to the secured system or data.
Despite the promising aspects of EEG-based authentication systems, several challenges remain. These include the variability of EEG signals due to factors such as user state (e.g., alertness, stress levels), the need for high-quality EEG headsets that are comfortable for continuous wear, and concerns regarding the privacy and security of biometric data. Moreover, the complexity of signal processing and feature extraction algorithms necessitates ongoing research to enhance the accuracy, speed, and reliability of EEG-based authentication systems.
In light of the above discussion, there exists an urgent need for solutions that overcome the limitations associated with conventional authentication systems. The proposed system, which incorporates an EEG headset for detecting brainwave signals and a sophisticated signal processing and authentication framework, addresses these needs by offering a secure, reliable, and non-invasive method for user authentication.

Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
A system (100) for authenticating a user has been developed, leveraging an electroencephalogram (EEG) headset (102) specifically adapted to detect brainwave signals from a user and to output corresponding EEG signal sources. The heart of the system lies within a signal processing unit (104), which is configured to receive said EEG signal sources from the EEG headset (102). This unit is composed of a signal acquisition module constructed to acquire said EEG signal sources, a signal enhancement module designed to amplify and filter said acquired EEG signal sources, and a feature extraction module configured to extract characteristic features from said enhanced EEG signal sources. An authentication service module (106) is then configured to receive said extracted features. This module comprises an authentication algorithm module adapted to compare said extracted features against pre-stored user profiles within a user database, and a user database structured to store and maintain said user profiles. The authentication algorithm module is further adapted to authenticate said user based on a comparison between said extracted features and corresponding features in said user profiles.
In an embodiment, said EEG headset (102) further comprises a calibration module (108), configured to calibrate said EEG headset (102) based on a user's baseline brainwave signals to enhance the accuracy of said EEG signal sources output. In an embodiment, said signal processing unit (104) further comprises a real-time monitoring module (110), configured to monitor the signal quality and provide feedback for re-acquisition of said EEG signal sources in case of detection of signal degradation. In an embodiment, said signal processing unit (104) further comprises a data encryption module (112), adapted to encrypt said EEG signal sources and said extracted features during transmission to said authentication service module (106) for enhanced security. In an embodiment, said signal enhancement module of said signal processing unit (104) further comprises an adaptive filtering module (114), configured to dynamically adjust filtering parameters based on the type of brainwave signals detected to optimize the signal-to-noise ratio.
In an embodiment, said authentication algorithm module of said authentication service module (106) further comprises a continuous learning module (116), constructed to update said user profiles with new EEG patterns for continuous improvement of the authentication accuracy. In an embodiment, said authentication service module (106) further comprises a multi-factor authentication module (118), adapted to require additional verification using a secondary authentication method alongside said extracted features for enhanced security. In an embodiment, said authentication service module (106) further comprises a user interface module (120), configured to provide authentication status and prompts to said user for interaction during the authentication process. In an embodiment, said user database of said authentication service module (106) further comprises a backup and recovery module (122), configured to periodically back up said user profiles and recover them in the event of data loss, thus ensuring the reliability of said authentication service module (106).
Furthermore, a method (200) for authenticating a user has been introduced, involving detecting brainwave signals from a user via an electroencephalogram (EEG) headset (102). This method includes outputting corresponding EEG signal sources from said EEG headset (102), receiving said EEG signal sources in a signal processing unit (104), and acquiring said EEG signal sources via a signal acquisition module of said signal processing unit (104). The process continues with amplifying and filtering said acquired EEG signal sources via a signal enhancement module of said signal processing unit (104), extracting characteristic features from said enhanced EEG signal sources via a feature extraction module of said signal processing unit (104), comparing said extracted features against pre-stored user profiles within a user database of an authentication service module (106), and authenticating said user based on a comparison between said extracted features and corresponding features in said user profiles of the authentication service module (106). This comprehensive approach not only ensures a high degree of security but also offers a seamless and user-friendly authentication experience.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a system for authenticating a user, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a method for authenticating a user, in accordance with the embodiments of the present disclosure.
FIG. 3 illustrates a basic architecture of electroencephalogram (EEG) based security authentication system, in accordance with the embodiments of the present disclosure.
FIG. 4 illustrates a detailed architecture of electroencephalogram (EEG) based security authentication system, in accordance with the embodiments of the present disclosure.
Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
FIG. 1 illustrates a system (100) for authenticating a user, in accordance with the embodiments of the present disclosure. The system (100) has been meticulously designed, comprising several integral components each fulfilling a unique function within the overall authentication framework. At the forefront of this system is an electroencephalogram (EEG) headset (102), meticulously adapted to capture brainwave signals emanating from a user. The functionality of the EEG headset (102) extends to the conversion of these brainwave signals into corresponding EEG signal sources, setting the foundational stage for the authentication process. The ability of the EEG headset (102) to accurately detect and output brainwave signals is paramount, as these signals embody the distinct neurological patterns that are central to the authentication of the user.
Following the initial acquisition of brainwave signals, the system encompasses a signal processing unit (104) that is adeptly configured to interface with the EEG headset (102). This unit is subdivided into several modules, each tasked with a specific aspect of signal processing to refine the brainwave data for authentication purposes. Initially, a signal acquisition module is employed, its construction specifically tailored to acquire the EEG signal sources from the headset. The critical role of this module cannot be overstated, as it ensures the seamless transition of raw brainwave data into the processing unit, laying the groundwork for further signal enhancement.
Subsequent to acquisition, the signal enhancement module within the signal processing unit takes precedence, engaging in the amplification and filtration of the acquired EEG signal sources. The primary objective of this module is the eradication of extraneous noise and the amplification of the signal to ensure that the brainwave data is of the highest fidelity, thus facilitating a more accurate extraction of characteristic features. The signal enhancement process is essential for isolating the user-specific data from the myriad of background noise that could potentially obscure the unique brainwave patterns.
The culmination of the signal processing effort is manifested in the feature extraction module, which is configured with the express purpose of extracting distinctive features from the enhanced EEG signal sources. This module employs advanced analytical techniques to discern the unique characteristics within the EEG signals that are indicative of the individual's neural activity. The extracted features represent the core data used for authentication, encapsulating the unique identifiers of the user's brainwave patterns.
The authentication service module (106) stands as the final arbiter in the authentication process, receiving the distilled features from the signal processing unit. Within this module, an authentication algorithm module is adeptly adapted to undertake the critical comparison of the extracted features against a pre-established repository of user profiles housed within a user database. This database is a structured compilation of user profiles, each containing a unique set of features derived from prior EEG signal analyses, serving as a benchmark for authentication. The authentication algorithm module leverages sophisticated computational techniques to ascertain the degree of correspondence between the extracted features and the database profiles, thus determining the authenticity of the user. This comparison is not merely a binary process but a nuanced evaluation that ensures the authenticated user's identity aligns with a high degree of accuracy to the stored profiles.
Through the systematic orchestration of these components, the system (100) for authenticating a user establishes a robust framework for secure user identification. By harnessing the intrinsic uniqueness of brainwave patterns through EEG technology, this system introduces a novel and highly secure method of authentication that significantly mitigates the risk of unauthorized access. The process from signal detection to user authentication exemplifies a seamless integration of technology and biometrics, offering a futuristic solution to the challenges of digital security.
In an embodiment, the system (100) is enhanced with the integration of a calibration module (108) within the EEG headset (102). This module is configured to calibrate the EEG headset (102) based on a user's baseline brainwave signals. The calibration process is designed to fine-tune the EEG headset (102) to the unique neurological patterns of an individual, thereby enhancing the accuracy of the EEG signal sources output. The calibration module (108) employs algorithms to adjust the headset's sensitivity and signal processing parameters, ensuring that the brainwave signals captured are a true representation of the user's cognitive state. This personalized calibration is critical for reducing errors in signal interpretation and improving the reliability of the authentication process. By adapting the EEG headset (102) to recognize and account for the inherent variability in brainwave signals among different users, the calibration module (108) significantly enhances the system's ability to accurately authenticate users based on their unique neurological patterns.
In another embodiment, the signal processing unit (104) is augmented with a real-time monitoring module (110). This module is configured to continually assess the quality of the EEG signal sources and provide feedback for the re-acquisition of said signals in the event of detected signal degradation. The real-time monitoring module (110) plays a crucial role in maintaining the integrity of the data used for authentication by ensuring that only high-quality signals are processed and analyzed. Should the module detect a compromise in signal quality, possibly due to poor electrode contact or external interference, it initiates a prompt to re-acquire the signal, thereby safeguarding against the authentication of users based on flawed or inadequate data. This proactive approach to signal quality management is instrumental in preserving the fidelity of the EEG signal sources, ensuring that the authentication process remains robust and reliable.
In a further embodiment, the signal processing unit (104) incorporates a data encryption module (112). This module is adapted to encrypt the EEG signal sources and the extracted features during their transmission to the authentication service module (106). The encryption of data in transit is a critical security measure, designed to protect the sensitive neurological information of users from interception or unauthorized access. By employing advanced encryption algorithms, the data encryption module (112) ensures that the confidentiality and integrity of the EEG signal sources and extracted features are maintained, providing an additional layer of security to the authentication process. This enhanced security measure is pivotal in fostering trust in the system's ability to safeguard user data, thereby encouraging the adoption of this novel authentication method.
In yet another embodiment, the signal enhancement module of the signal processing unit (104) is further refined with the addition of an adaptive filtering module (114). Configured to dynamically adjust filtering parameters based on the type of brainwave signals detected, the adaptive filtering module (114) optimizes the signal-to-noise ratio. This dynamic adjustment is key to accommodating the diverse range of brainwave signals that can be encountered, each with its own characteristic frequency and amplitude. By tailoring the filtering process to the specific attributes of the detected signals, the adaptive filtering module (114) significantly improves the clarity and quality of the EEG signal sources, facilitating a more precise extraction of the characteristic features essential for user authentication.
In an additional embodiment, the authentication algorithm module of the authentication service module (106) is equipped with a continuous learning module (116). Constructed to update user profiles with new EEG patterns, this module enables the continuous improvement of the authentication accuracy. The continuous learning module (116) applies machine learning algorithms to analyze the EEG signal sources and extracted features, identifying and integrating any novel patterns or variations into the existing user profiles. This process of continuous learning and adaptation ensures that the authentication algorithm remains sensitive to the evolving neurological patterns of users, thereby enhancing the system's ability to accurately authenticate users over time.
In a subsequent embodiment, the authentication service module (106) is further enhanced with a multi-factor authentication module (118). Adapted to require additional verification using a secondary authentication method alongside the extracted features, this module introduces an extra layer of security to the authentication process. The incorporation of multi-factor authentication ensures that even if one authentication factor is compromised, unauthorized access is still prevented by the requirement of an additional verification step. This robust approach to security significantly enhances the overall integrity of the authentication system, providing users with heightened protection against potential security breaches.
In another embodiment, the authentication service module (106) is complemented by a user interface module (120). Configured to provide authentication status and prompts to the user for interaction during the authentication process, this module serves as the primary point of communication between the system and the user. The user interface module (120) presents a user-friendly platform for users to engage with the authentication process, offering clear instructions and feedback to ensure a seamless and intuitive experience. This direct engagement not only facilitates the practical application of the system but also enhances user satisfaction by making the authentication process more accessible and understandable.
In a final embodiment, the user database of the authentication service module (106) incorporates a backup and recovery module (122). Configured to periodically back up user profiles and recover them in the event of data loss, this module ensures the reliability of the authentication service module (106). The backup and recovery module (122) implements a comprehensive data management strategy, safeguarding against the loss of critical user information due to hardware failure, software malfunction, or other unforeseen incidents. By ensuring that user profiles can be reliably restored, the backup and recovery module (122) maintains the continuity and effectiveness of the authentication process, preserving the trust and confidence of users in the system's resilience and reliability.
FIG. 2 illustrates a method (200) for authenticating a user, in accordance with the embodiments of the present disclosure. At step (202) method (200) for authenticating a user begins with the detection of brainwave signals via an electroencephalogram (EEG) headset (102) placed on the user's head, capturing the user's neurological activity. At step (204) the EEG headset (102) outputs the captured brainwave signals as corresponding EEG signal sources, translating the neurological activity into a digital format. At step (206) these EEG signal sources are then transmitted to a signal processing unit (104), where they are received and prepared for further analysis and processing. At step (208) within the signal processing unit (104), a signal acquisition module takes charge, acquiring the EEG signal sources to ensure they are correctly captured and ready for enhancement. At step (210) the EEG signal sources undergo amplification and filtering through a signal enhancement module of the signal processing unit (104), improving signal quality and clarity. At step (212) a feature extraction module within the signal processing unit (104) extracts characteristic features from the enhanced EEG signal sources, isolating specific patterns indicative of the user's identity. At step (214) the extracted features are then compared against pre-stored user profiles within a user database of an authentication service module (106), searching for a match. At step (216) the user is authenticated based on the comparison between the extracted features and the corresponding features in the user profiles within the authentication service module (106), completing the authentication process.
FIG. 3 illustrates a basic architecture of electroencephalogram (EEG) based security authentication system, in accordance with the embodiments of the present disclosure. The process commences with the EEG headset, which is responsible for capturing EEG signal sources emanating from the user. These signals, representative of the user's unique neurological patterns, are then conveyed to the signal processing segment. Herein, the process bifurcates into distinct phases: firstly, signal acquisition, where the raw EEG signals are captured; followed by signal enhancement, which purifies the signals by amplifying the pertinent features and suppressing noise; and finally, feature extraction, where critical data points are distilled from the EEG signals to establish a unique user profile. The extracted features are subsequently fed into the authentication service, where an authentication algorithm undertakes the crucial task of comparing these features against those stored in the user database. This database serves as a secure repository of pre-verified user profiles. If the authentication algorithm determines a congruence between the incoming data and an existing profile, the system confirms the user's identity, thereby granting access to the secured services or premises. This streamlined process encapsulates a sophisticated approach to security, harnessing the intrinsic biometric data of brainwave patterns for robust and reliable user authentication.
FIG. 4 illustrates a detailed architecture of electroencephalogram (EEG) based security authentication system, in accordance with the embodiments of the present disclosure. In this system, the user initiates the authentication process by donning the EEG headset, which is designed to capture the unique EEG signals generated by their brain activity. Once the EEG signals are captured, they are sent to the authentication server, a central unit responsible for processing these signals. The authentication server is equipped with advanced algorithms capable of processing and analyzing the EEG signals, extracting distinctive features intrinsic to the user. These features are then compared with a pre-established set of user profiles within the system's database to find a matching profile. If a match is found, the authentication is deemed successful, and a signal is sent to the user interface, which then grants the user access to the secure area. Conversely, if the EEG signals do not match the profiles in the database, the authentication is considered unsuccessful, and access is denied, with the user interface indicating the failure of authentication. This innovative system leverages the unique nature of EEG signals for robust security authentication, ensuring secure and personalized access control to restricted areas or information.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We claims:

A system (100) for authenticating a user, comprising:
an electroencephalogram (EEG) headset (102) adapted to detect brainwave signals from a user and output corresponding EEG signal sources;
a signal processing unit (104) configured to receive said EEG signal sources from said EEG headset (102), comprising:
a signal acquisition module constructed to acquire said EEG signal sources;
a signal enhancement module designed to amplify and filter said acquired EEG signal sources; and
a feature extraction module configured to extract characteristic features from said enhanced EEG signal sources;
an authentication service module (106) configured to receive said extracted features, comprising:
an authentication algorithm module adapted to compare said extracted features against pre-stored user profiles within a user database; and
a user database structured to store and maintain said user profiles, wherein said authentication algorithm module is further adapted to authenticate said user based on a comparison between said extracted features and corresponding features in said user profiles.
The system (100) of claim 1, wherein said EEG headset (102) further comprises a calibration module (108), configured to calibrate said EEG headset (102) based on a user's baseline brainwave signals to enhance the accuracy of said EEG signal sources output.
The system (100) of claim 1, wherein said signal processing unit (104) further comprises a real-time monitoring module (110), configured to monitor the signal quality and provide feedback for re-acquisition of said EEG signal sources in case of detection of signal degradation.
The system (100) of claim 1, wherein said signal processing unit (104) further comprises a data encryption module (112), adapted to encrypt said EEG signal sources and said extracted features during transmission to said authentication service module (106) for enhanced security.
The system (100) of claim 1, wherein said signal enhancement module of said signal processing unit (104) further comprises an adaptive filtering module (114), configured to dynamically adjust filtering parameters based on the type of brainwave signals detected to optimize the signal-to-noise ratio.
The system (100) of claim 1, wherein said authentication algorithm module of said authentication service module (106) further comprises a continuous learning module (116), constructed to update said user profiles with new EEG patterns for continuous improvement of the authentication accuracy.
The system (100) of claim 1, wherein said authentication service module (106) further comprises a multi-factor authentication module (118), adapted to require additional verification using a secondary authentication method alongside said extracted features for enhanced security.
The system (100) of claim 1, wherein said authentication service module (106) further comprises a user interface module (120), configured to provide authentication status and prompts to said user for interaction during the authentication process.
The system (100) of claim 1, wherein said user database of said authentication service module (106) further comprises a backup and recovery module (122), configured to periodically back up said user profiles and recover them in the event of data loss, thus ensuring the reliability of said authentication service module (106).
A method (200) for authenticating a user, comprising:
detecting brainwave signals from a user via an electroencephalogram (EEG) headset (102);
outputting corresponding EEG signal sources from said EEG headset (102);
receiving said EEG signal sources in a signal processing unit (104);
acquiring said EEG signal sources via a signal acquisition module of said signal processing unit (104);
amplifying and filtering said acquired EEG signal sources via a signal enhancement module of said signal processing unit (104);
extracting characteristic features from said enhanced EEG signal sources via a feature extraction module of said signal processing unit (104);
comparing said extracted features against pre-stored user profiles within a user database of an authentication service module (106); and
authenticating said user based on a comparison between said extracted features and corresponding features in said user profiles of the authentication service module (106).

SYSTEM AND METHOD FOR AUTHENTICATING A USER

The present disclosure provides a system (100) for authenticating a user, comprising an electroencephalogram (EEG) headset (102) adapted to detect brainwave signals from a user and output corresponding EEG signal sources; a signal processing unit (104) configured to receive said EEG signal sources from said EEG headset (102), comprising a signal acquisition module constructed to acquire said EEG signal sources; a signal enhancement module designed to amplify and filter said acquired EEG signal sources; and a feature extraction module configured to extract characteristic features from said enhanced EEG signal sources; an authentication service module (106) configured to receive said extracted features, comprising an authentication algorithm module adapted to compare said extracted features against pre-stored user profiles within a user database; and a user database structured to store and maintain said user profiles, wherein said authentication algorithm module is further adapted to authenticate said user based on a comparison between said extracted features and corresponding features in said user profiles.
Fig. 1

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FIG. 3
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FIG. 4
, Claims:I/We claims:

A system (100) for authenticating a user, comprising:
an electroencephalogram (EEG) headset (102) adapted to detect brainwave signals from a user and output corresponding EEG signal sources;
a signal processing unit (104) configured to receive said EEG signal sources from said EEG headset (102), comprising:
a signal acquisition module constructed to acquire said EEG signal sources;
a signal enhancement module designed to amplify and filter said acquired EEG signal sources; and
a feature extraction module configured to extract characteristic features from said enhanced EEG signal sources;
an authentication service module (106) configured to receive said extracted features, comprising:
an authentication algorithm module adapted to compare said extracted features against pre-stored user profiles within a user database; and
a user database structured to store and maintain said user profiles, wherein said authentication algorithm module is further adapted to authenticate said user based on a comparison between said extracted features and corresponding features in said user profiles.
The system (100) of claim 1, wherein said EEG headset (102) further comprises a calibration module (108), configured to calibrate said EEG headset (102) based on a user's baseline brainwave signals to enhance the accuracy of said EEG signal sources output.
The system (100) of claim 1, wherein said signal processing unit (104) further comprises a real-time monitoring module (110), configured to monitor the signal quality and provide feedback for re-acquisition of said EEG signal sources in case of detection of signal degradation.
The system (100) of claim 1, wherein said signal processing unit (104) further comprises a data encryption module (112), adapted to encrypt said EEG signal sources and said extracted features during transmission to said authentication service module (106) for enhanced security.
The system (100) of claim 1, wherein said signal enhancement module of said signal processing unit (104) further comprises an adaptive filtering module (114), configured to dynamically adjust filtering parameters based on the type of brainwave signals detected to optimize the signal-to-noise ratio.
The system (100) of claim 1, wherein said authentication algorithm module of said authentication service module (106) further comprises a continuous learning module (116), constructed to update said user profiles with new EEG patterns for continuous improvement of the authentication accuracy.
The system (100) of claim 1, wherein said authentication service module (106) further comprises a multi-factor authentication module (118), adapted to require additional verification using a secondary authentication method alongside said extracted features for enhanced security.
The system (100) of claim 1, wherein said authentication service module (106) further comprises a user interface module (120), configured to provide authentication status and prompts to said user for interaction during the authentication process.
The system (100) of claim 1, wherein said user database of said authentication service module (106) further comprises a backup and recovery module (122), configured to periodically back up said user profiles and recover them in the event of data loss, thus ensuring the reliability of said authentication service module (106).
A method (200) for authenticating a user, comprising:
detecting brainwave signals from a user via an electroencephalogram (EEG) headset (102);
outputting corresponding EEG signal sources from said EEG headset (102);
receiving said EEG signal sources in a signal processing unit (104);
acquiring said EEG signal sources via a signal acquisition module of said signal processing unit (104);
amplifying and filtering said acquired EEG signal sources via a signal enhancement module of said signal processing unit (104);
extracting characteristic features from said enhanced EEG signal sources via a feature extraction module of said signal processing unit (104);
comparing said extracted features against pre-stored user profiles within a user database of an authentication service module (106); and
authenticating said user based on a comparison between said extracted features and corresponding features in said user profiles of the authentication service module (106).

SYSTEM AND METHOD FOR AUTHENTICATING A USER