Biometric Intent Recognition (BIR) uses brain-computer interface (BCI) technology and gaze tracking to analyze subtle physiological signals, particularly from the pre-frontal cortex, to predict an individual's malicious intent before they take action.
Traditional cybersecurity measures often fail to detect intent before an action is taken. BIR aims to fill this gap by providing real-time, pre-emptive threat detection based on physiological signals.
BIR systems employ BCI sensors to capture electroencephalogram (EEG) data from the scalp, which is then processed using machine learning algorithms to identify patterns associated with deceptive or malicious intentions. Gaze tracking is also used to monitor eye movements and correlate them with cognitive states that might indicate a threat.
Manufacturing involves the development of miniaturized BCI sensors and integration with gaze tracking hardware. The process requires precision in sensor placement and calibration for accurate signal acquisition.
The build process includes designing and fabricating BCI sensors, calibrating them to specific individuals or groups, integrating them with gaze tracking systems, and developing machine learning models to interpret the data.
Field units draw low hundreds of watts; fabrication is energy-intensive due to vacuum baking processes required for sensor manufacturing.
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