EEG Human Detector for Bot Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for distinguishing human inputs from bot access, such as CAPTCHA, are ineffective against sophisticated automated processes and can be inaccessible or burdensome for users with disabilities, leading to potential loss of network resource access.
Innovation Solution
A computerized method using electroencephalogram (EEG) signals to determine human presence by comparing obtained signals to trained profiles, leveraging neuronal responses to sensory information to differentiate humans from bots through an Artificial Neural Network (ANN) analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If CAPTCHA tools are used to distinguish human inputs from bot access, then security against bots is improved, but accessibility for users with disabilities deteriorates
Solution Approach 1:
The patent replaces the mechanical/visual interaction system of CAPTCHA (requiring visual processing and manual input) with a physiological signal-based system that automatically captures EEG brain waves. This substitution eliminates the need for users to visually process distorted text or perform manual typing tasks, thereby maintaining bot detection capability while improving accessibility for users with visual or motor disabilities.
Solution Approach 2:
The system leverages the user's own physiological signals (brain waves) as the authentication mechanism. The user's brain naturally generates EEG patterns in response to presented content, and this self-generated physiological response serves as the verification method. This self-service approach eliminates the need for external assistance or specialized adaptive technologies, making the system inherently accessible while maintaining security.
2Reliability
If CAPTCHA challenges are made more complex to defeat sophisticated bots, then security against automated processes is improved, but user completion time increases
Solution Approach 1:
The patent replaces the time-consuming mechanical task of manually decoding and typing distorted CAPTCHA text with an automated physiological signal capture process. The system automatically presents content, captures the user's EEG response, and processes the brain wave patterns to verify humanity. This substitution eliminates the manual effort and time required for complex CAPTCHA challenges while maintaining or improving detection accuracy through sophisticated analysis of neural responses.
3Ease of operation
If audio alternatives are provided for visually impaired users, then accessibility is improved, but reliability of verification deteriorates
Solution Approach 1:
The system uses the user's own physiological response (EEG brain waves) as the verification mechanism, which is independent of sensory modality. Whether the user experiences visual, auditory, or other sensory input, their brain generates characteristic EEG patterns in response. This self-service approach using physiological signals maintains verification reliability across all user types, including visually impaired users, without requiring separate audio alternatives that compromise security.
Data Source
AI summary
Aspects provide human detector devices based on neuronal response, wherein the devices are configured to obtain electroencephalogram signals from an entity during a presentation of first sensory information to the entity, and compares the obtained electroencephalogram signals to each of a plurality of trained electroencephalogram signal profile portions that are labeled as the first sensory information that represent electroencephalogram signals most commonly generated by different persons as a function of presentation to the persons of sensory information corresponding to the first sensory information. Thus, the configured processor determines whether the entity is a human as a function of a strength of match of the obtained electroencephalogram signals to ones of the trained electroencephalogram signal profile portions labeled as first sensory information that have highest most-common weightings.