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The Naos capsule is a biometrics service station, where the user enters and is "hooked up" to physiological monitoring (biometrics) equipment. After being safely secured within the capsule, the user undergoes a test procedure, while the biometrics data is recorded and stored in a database. After first gauging the participant's physiological data for five seconds, in order to establish baseline readings, an image is projected onto the capsule’s ceiling. The participant's physiological response to the image is measured. Then via statistical classification and depending on the specific test being administered, the participant is rated and placed into one of several possible categories. Another image whose data is closest to the measured physiological data is then shown. This loop continues in an attempt to achieve "equilibrium" - a point where the image's expected physiological response and classification and the participant's actual response and classification are the same. Thus, in a circular relationship, the system influences the participant's body and brain that in turn influences the system. Examples include being classified as "aggressive" or being given a "loyalty" rating of 0.5. This process of classification is accomplished via a machine-learning algorithm.
The Biometric Tendency Recognition and Classification System (BTRCS)™ is a software application that continuously measures a participant's physiological responses to a given image and runs a statistical classification algorithm on the measured data that then classifies the participant into one of several predetermined categories. BTRCS forms the core of the Naos system. It includes a database of images that can be customized based upon the particular test being administrated. The classification is achieved using a K-nearest neighbor algorithm. This machine-learning algorithm measures the distance between the participant's physiological responses to images and the responses that are in the database - what is known as the training data. With each new participant, new training data is continually being added to the system.
The current system focuses on dividing the brain's activity into three distinct frequency bands (alpha, beta and theta). These bands are analyzed and used to discern the participant's levels of anxiety, attention and meditation. These levels are then mapped to 3-dimensional visualizations using NURBS, or Non-uniform rational B-splines. In addition, the GSR level is mapped to a 3-dimensional animating shape that increases in size as the GSR level increases. The test stimuli are also shown in correlation with the visualized biometric data.
The Naos Loyalty Test™ is an example of the type of test that can be administered using the Naos Platform. This test classifies individuals based on their reactions to images of people of different races coupled with questions intended to elicit emotional responses. The biometric reactions are then used to determine their level of prejudice on a "loyalty scale".