Brain-Computer Interface Authentication With Time-Series Challenges
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Solution Overview
Problem
Existing brain-computer interfaces lack effective safeguards to prevent unauthorized access, malicious interference, and ensure the integrity, confidentiality, and safety of brain signals, which are critical for widespread adoption and secure interaction with computing systems.
Innovation Solution
A time-sensitive authentication system using a long-short-term memory neural network and autoencoder to generate stimulus-response pairs, which are used to challenge and authenticate brain signals, preventing unauthorized communication and ensuring the integrity and confidentiality of brain-computer interface communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If brain-computer interface devices are developed for direct control of computing systems, then ease of operation and productivity are improved, but security and reliability deteriorate due to lack of authentication safeguards
Solution Approach 1:
The system performs preliminary authentication by capturing brain activity responses to challenges before allowing any communication or control operations. The authenticator verifies the user's identity in advance by comparing captured brain responses against predicted responses generated by the neural network, ensuring security is established before operational access is granted.
Solution Approach 2:
The patent introduces an authenticator as an intermediary component between the brain-computer interface and the computing system. This mediator captures brain activity, compares it with predicted responses from the neural network, and only permits communication when authentication succeeds, thereby adding security without directly interfering with the user's ability to control the system through brain signals.
2Reliability
If authentication systems are added to brain-computer interfaces, then reliability and security are improved, but device complexity increases
Solution Approach 1:
The neural network serves multiple functions: it generates predicted brain responses for authentication, learns from captured brain activity patterns, and adapts to individual user characteristics. This multi-functionality reduces the need for separate dedicated components for each task, thereby limiting the increase in overall system complexity while maintaining strong authentication capabilities.
Solution Approach 2:
The authenticator and neural network operate autonomously by automatically capturing brain activity, comparing responses, and making authentication decisions without requiring external intervention or complex control mechanisms. The system self-regulates the authentication process, reducing the need for additional complex management infrastructure.
3Loss of information
If challenge-response authentication is implemented, then confidentiality and integrity are improved, but loss of time increases due to authentication delays
Solution Approach 1:
The system captures and processes brain activity responses to challenges in real-time before communication occurs. By performing the authentication verification preliminarily and continuously, the system ensures that integrity protection is in place without requiring repeated authentication delays during actual communication operations.
Solution Approach 2:
The authenticator operates continuously, maintaining an ongoing verification of brain activity patterns rather than performing discrete periodic authentication. This continuous operation ensures that confidentiality and integrity are maintained without interrupting the flow of communication, as the authentication is an ongoing background process rather than a series of interruptions.
Data Source
AI summary
A method and apparatus to control operation of a brain-computer interface, comprising: capturing, at a sensor, a time series of brain activity in response to stimuli; passing data for the time series of brain activity to a history-based challenge generator; receiving, from the history-based challenge generator, a challenge comprising a generated stimulus with a predicted brain response derived from data for the time series of brain activity; issuing the challenge over the brain-computer interface; capturing, at the sensor, a brain response to the challenge; comparing, by an authenticator, the brain response to the challenge with the predicted brain response for the generated stimulus; and responsive to finding no match between the brain response to the challenge and the predicted brain response, preventing further activity over the brain-computer interface.