EP-4736131-A1 - NEURAL BIOMETRIC AUTHENTICATION SYSTEM AND METHOD

Abstract

The system (1000) and method are based on the generation of at least one individual biometric identifier – IBI - from at least user representative synthetic data {N s } (S70), said user representative synthetic data being generated from neural data {N} of said user (10) received by a pre- trained generative model. Said at least one IBI enables to authenticate said user (10) when compared to at least one user's signature stored in a memory (40). Said at least one IBI can replace both identifier and password to authenticate and login said user (10) without containing any personal biometric data, guaranteeing the anonymity of the login data that can be exchanged remotely.

Inventors

• SEMAH, Thomas
• NAVARRO SUNE, Xavier

Assignees

• Dotsify

Dates

Publication Date

20260506

Application Date

20240628

Claims (1)

1. CLAIMS 1. A neural biometric authentication system (1000) for securely authenticating a user (10), the system comprising: • a receiving module (100) configured to receive biometric data (20) of said user (10), said biometric data (20) comprising at least neural data {Nr} of said user (10), said neural data {Nr} comprising at least one neural signal S N function of time and of at least one channel, said at least one channel covering a predefined area of said user’s head (S10); • an encoding module (200) comprising: - an augmentation module (230) configured to generate user representative synthetic data {N s } by using at least one first generative model configured to receive as input said received neural data {Nr}, wherein said at least one first generative model has been previously pre-trained on neural data obtained from a plurality of subjects at different mental states, from different recording devices, and acquired by at least two channels (S60); - an identity – ID – generating module (250) configured to generate a set of individual biometric identifiers – IBI – comprising at least one first individual biometric identifier – IBI –, said at least one first IBI being generated from at least said generated user representative synthetic data {N s } and said received neural data {N r } using a biometric identity – ID – model, said biometric ID model being a previously trained Transformer-based model (S70); • a recognition module (300) comprising an identity – ID – management module (320) configured to, depending on a predetermined condition: - compare said set of individual biometric identifiers – IBI to at least one user’s signature stored in a memory (40) and output an authentication result (S100); or - save said set of individual biometric identifiers – IBI as at least one user’s signature (S110) in said memory (40). 2. The system according to claim 1, wherein said at least one generative model is configured for generating said user representative synthetic data {Ns} comprising synthetic neural signals function of time and of at least one synthetic channel, said at least one synthetic channel covering an area of said user’s head different from said specific area covered by said at least one channel of said received neural data {Nr}. 3. The system according to either one of claim 1 or 2, wherein said at least one generative model is a Generative Adversarial Network – GAN – based model using a convolutional neural network architecture, said at least one generative model being fine-tuned with an enrollment dataset, and wherein said enrollment dataset comprises recording neural data of said user (10) at various mental states and/or at various times of a day. 4. The system according to any one of claim 1 to 3, wherein said biometric data (20) further comprises physiological data {P} and/or anatomical data {A}. 5. The system according to any one of claim 4, wherein the identity – ID – generating module (250) is further configured to generate: • a second IBI obtained from said physiological data {P}and/or a third IBI obtained from said anatomical data {A}, wherein the second IBI and/or the third IBI are comprised in the set of IBI; and/or • a first mixed IBI obtained from a combination of at least said at least one first IBI being generated from said user representative synthetic data {Ns} and said received neural data {Nr}, said second IBI obtained from said physiological data {P} and/or said third IBI obtained from said anatomical data {A}, wherein the first mixed IBI is comprised in the set of IBI. 6. The system according to any one of claim 1 to 5, further comprising: • a pre-processing module (210) configured to: - pre-process said received biometric data (20) by applying at least one of: a bandpass filter, an alignment, an artifact removal, and/or a standardization (S30); - analyze said received biometric data (20), respectively said pre- processed biometric data when available, and compute a quality parameter (S20); and/or wherein the augmentation module (230) is configured to receive said pre- processed and/or analyzed neural data {N p/a } and, to generate said user representative synthetic data {Ns} by using said pre-processed and/or analyzed neural data {Np/a} instead of the received neural data {Nr}. 7. The system according to any one of claims 1 to 6, further comprising: • a transmission module (260) configured to encrypt each individual biometric identifier – IBI – of the set of IBI and transmit each encrypted IBI through a communication network (30) (S80); • a decrypting module (310) configured to receive and decrypt each encrypted IBI (S90); wherein, depending on said predetermined condition, said ID management module (320) is configured to: • compare each decrypted IBI to at least one user’s signature stored in said memory (40) and output the authentication result (S100); or • save said each decrypted IBI as at least one user’s signature in said memory (40) (S110). 8. The system according to any one of claims 4 to 7, further comprising a conversion module (220) configured to convert at least one among said neural data {N}, said physiological data {P}, and said anatomical data {A}, into respectively a neural converted data, a physiological converted data and/or anatomical converted data, wherein said neural converted data is a 1D or 2D representation of said neural data {N}, said physiological converted data is a 1D or 2D representation of said physiological data {P} and said anatomical converted data is a 1D or 2D representation of said anatomical data {A} (S40). 9. The system according to claim 8, wherein: • the augmentation module (230) further comprises at least one second generative model configured to receive as input the at least one converted data representative of said neural data {N} and to generate second user representative synthetic data {Is}; and • the identity – ID – generating module (250) is further configured to generate: i. a first complex IBI obtained from said second user representative synthetic data {Is} and the at least one converted data representative of said neural data {N}, the first complex IBI being comprised in the set of IBI; and/or ii. a second complex IBI obtained from the at least one converted data representative of said physiological data {P} and/or a third complex IBI obtained from the at least one converted data representative of said anatomical data {A}, the second complex IBI and the third complex IBI being comprised in the set of IBI; and/or iii. a second mixed IBI obtained from a combination of at least said first complex IBI, and at least one among said second complex IBI and third complex IBI, the second mixed IBI being comprised in the set of IBI. 10. The system according to claim 8, further comprising: • a fusion module (210) configured to merge the at least two among converted data obtained for said neural data {Nr}, said physiological data {P}, said anatomical data {A} into a unique converted data {INPA} by using at least one fusion model, said unique converted data being a 1D or 2D representation of a combination of said neural data {N}, said physiological data {P} and/or said anatomical data {A} (S50), wherein, the identity – ID – generating module (250) is further configured to generate, depending on a third configuration parameter: • at least one conjugated IBI obtained from said unique converted data, the at least one conjugated IBI being comprised in the set of IBI; and/or • a third mixed IBI obtained from a combination of at least said user representative synthetic data {Ns} and said neural data {Nr}, and said unique converted data, the third mixed IBI being comprised in the set of IBI. 11. The system according to any one of claims 4, 5, 8, 9 or 10, wherein: • the physiological data {P} comprise at least one of: a first optical signal and/or an electrical signal representative of a measurement of a vascular or muscular function of the user (10), such as a photoplethysmyography or an electromyography, and/or a first acoustic signal such as a bioacoustics-based signal like the user’s voice, coming from at least one specific physiological sensor; and • the anatomical data {A} comprise at least one of: a second optical signal and/or a second acoustic signal representative of morphologic information or density information relative to the user (10), such as a broadband ultrasound attenuation, quantitative ultrasounds, bioacoustics-based techniques, a computed-tomography image or a magnetic resonance image, coming from at least one specific anatomical sensor. 12. The system according to any one of the preceding claims, wherein the receiving module (100) is also configured to receive at least one external user identifier {E ID } (S130), and wherein the ID generating module (250) is configured to generate a combined individual biometric identifier by combining said at least one individual biometric identifier to said at least one external user identifier {EID} (S70). 13. The system according to any one of the preceding claims, wherein said ID management module (320) is further configured to send to a recipient an information signal representative of said authentication result, said recipient being said user (10) or an external user interface, and said information signal being at least one of: a visual signal, an auditory signal, a kinesthetic signal, an olfactory signal, a gustative signal (S120). 14. The system according to any one of the preceding claims, further comprising: • a stimulation module configured to control an external stimulation device configured to deliver to said user (10) at least one sensory stimulus, said sensory stimulus being at least one of: a visual stimulus, an auditory signal, a kinesthetic signal, an olfactory stimulus; and wherein the receiving module is further configured to receive a feedback signal from said user (10) in response to said at least one delivered sensory stimulus. 15. The system according to any one of the preceding claims, wherein the system is embedded in an apparatus, said apparatus being one of: an earbud or earphone, a headband, an earring, a headset such as an augmented Reality – AR - headset, a Virtual Reality – VR - headset, a Mixed Reality – MR - headset, smart glasses. 16. A computer-implemented method for securely authenticating a user (10), the method comprising: • receiving biometric data (20) of said user (10), said biometric data (20) comprising at least neural data {Nr} of said user (10), said neural data {Nr} comprising at least one neural signal SN function of time and of at least one channel, said at least one channel covering a predefined area of said user’s head (S10); • generating user representative synthetic data {Ns} by using at least one first generative model configured to receive as input said received neural data {Nr}, wherein said at least one first generative model has been previously pre-trained on neural data obtained from a plurality of subjects at different mental states, from different recording devices, and acquired by at least two channels (S60); • generating generate a set of individual biometric identifiers – IBI – comprising at least one first individual biometric identifier – IBI –, said at least one first IBI being generated from at least said generated user representative synthetic data {Ns} and said received neural data {Nr} using a biometric identity – ID – model, said biometric ID model being a previously trained Transformer-based model (S70); and depending on a predetermined condition: - compare said set of individual biometric identifiers – IBI to at least one user’s signature stored in a memory (40) and output an authentication result (S100); or - save said set of individual biometric identifiers – IBI as at least one user’s signature (S110) in said memory (40).

Description

NEURAL BIOMETRIC AUTHENTICATION SYSTEM AND METHOD FIELD OF INVENTION [0001] The present invention relates to the general field of biometric identification, particularly to a neural biometric authentication system and method for providing a secure, user-specific multimedia experience using individual neural signatures. BACKGROUND OF INVENTION [0002] In today's digital era, online platforms and services have become an inseparable part of our everyday life. Users engage with a myriad of websites, applications, and services that necessitate authentication and access control. As it stands, the most prevalent method of user authentication is based on the creation and management of multiple accounts across diverse platforms, each with its unique login credentials (for example, usernames and passwords). This fragmented approach leads to several challenges and inconveniences for both users and service providers. [0003] Existing Limitations and Challenges: [0004] User Burden: Users face the challenge of remembering multiple usernames and passwords for different services, leading to password fatigue, increased security risks, and a higher likelihood of forgotten credentials. This burden discourages users from engaging with new platforms and can result in a poor user experience. [0005] Security Risks: Many users resort to using weak passwords or reusing passwords across multiple services due to the difficulty of managing multiple credentials. This practice poses significant security risks, as a data breach on one platform can expose user accounts on other services. [0006] Inefficiency for Service Providers: Managing user accounts, authentication systems, and password-related support requests can be resource-intensive for service providers. Additionally, they may face challenges in ensuring consistent security practices across their user base. [0007] Thus, there is a clear need for a universal login system that addresses the limitations and challenges mentioned above. [0008] Current methods of user recognition, such as facial recognition and voice recognition, often require the use of external devices, such as cameras or microphones. Palm print requires palmprint scanner or palmprint reader. Fingerprint requires fingerprint sensors. Retinal scanning requires a retinal scanner. Iris recognition requires an iris scanner, typically an electronic device equipped with a specialized camera and subtle infrared light. These methods can also be vulnerable to spoofing and other security threats. [0009] Thus, there is also a need for a universal login system that uses physiological data measured directly from the users to recognize them and grant access to their digital space. SUMMARY [0010] The present invention relates to neural biometric authentication system for securely authenticating a user , the system comprising: • a receiving module configured to receive biometric data of said user, said biometric data comprising at least neural data {Nr} of said user, said neural data {Nr} comprising at least one neural signal SN function of time and of at least one channel, said at least one channel covering a predefined area of said user’s head; • an encoding module comprising: - an augmentation module configured to generate user representative synthetic data {Ns} by using at least one first generative model configured to receive as input said received neural data {Nr}, wherein said at least one first generative model has been previously pre-trained on neural data obtained from a plurality of subjects at different mental states, from different recording devices, and acquired by at least two channels; - an identity – ID – generating module configured to generate a set of individual biometric identifiers – IBI – comprising at least one first individual biometric identifier – IBI –, said at least one first IBI being generated from at least said generated user representative synthetic data {Ns} and said received neural data {Nr} using a biometric identity – ID – model, said biometric ID model being a previously trained Transformer- based model; • a recognition module comprising an identity – ID – management module configured to, depending on a predetermined condition: - compare said set of individual biometric identifiers – IBI to at least one user’s signature stored in a memory (and output an authentication result; or - save said set of individual biometric identifiers – IBI as at least one user’s signature in said memory. [0011] According to other advantageous aspects of the invention, the system comprises one or more of the features described in the following embodiments, taken alone or in any possible combination. [0012] According to one embodiment, said at least one generative model is configured for generating said user representative synthetic data {Ns} comprising synthetic neural signals function of time and of at least one synthetic channel, said at least one synthetic channel covering an area of said user’s head different fr