Abstract: New human bio-sensors in a virtual reality headset can be worn comfortably for extended periods of time, detecting brain, nerve, ocular, muscle or other bio-signals. Recording raw data, amplification, digital conversion, filtering, support subsequent manipulation by iteratively creating multiple interpretation maps. Data processing and transmission are reduced so that control of a remote device may occur in real time, based on an event in the body of a wearer. A signal interpretation engine iteratively creates numerous interpretation maps, the best correlated one being used exclusively, thus minimizing false positives and requiring minimal data to distinguish an event from any other non-presence of that event. After training and verification, operation in real time processes live, continuous data to control a remote device based on the events detected by the sensor set in a headset, arm bands, leg bands, gloves, or boots.

Abstract: A signal interpretation engine apparatus and method are disclosed as including a computer programmed to run a feature expansion module, a weight table module, a consolidation module, and a map generation module. The feature expansion module contains feature operators for operating on a signal to expand the signal to form a feature map of feature segments. Each feature segment corresponds to a unique representation of the signal created by a feature operator from the signal across an epoch. An epoch corresponds to an event within a time segment. A consolidation module applies aggregators to consolidate the inner products or superposition segments into a distribution function representing an attribute over a domain reflecting a selected weight table, aggregator, and event type, corresponding to each value of the attribute.

Abstract: A signal interpretation engine apparatus and method are disclosed as including a computer programmed to run a feature expansion module, a weight table module, a consolidation module, and a map generation module. The feature expansion module contains feature operators for operating on a signal to expand the signal to form a feature map of feature segments. Each feature segment corresponds to a unique representation of the signal created by a feature operator from the signal across an epoch. An epoch corresponds to an event within a time segment. A consolidation module applies aggregators to consolidate the inner products or superposition segments into a distribution function representing an attribute over a domain reflecting a selected weight table, aggregator, and event type, corresponding to each value of the attribute.

Abstract: A signal interpretation engine apparatus and method are disclosed as including a computer programmed to run a feature expansion module, a weight table module, a consolidation module, and a map generation module. The feature expansion module contains feature operators for operating on a signal to expand the signal to form a feature map of feature segments. Each feature segment corresponds to a unique representation of the signal created by a feature operator from the signal across an epoch. An epoch corresponds to an event within a time segment. A consolidation module applies aggregators to consolidate the inner products or superposition segments into a distribution function representing an attribute over a domain reflecting a selected weight table, aggregator, and event type, corresponding to each value of the attribute.

Abstract: A signal interpretation engine apparatus and method are disclosed as including a computer programmed to run a feature expansion module, a weight table module, a consolidation module, and a map generation module. The feature expansion module contains feature operators for operating on a signal to expand the signal to form a feature map of feature segments. Each feature segment corresponds to a unique representation of the signal created by a feature operator from the signal across an epoch. An epoch corresponds to an event within a time segment. A consolidation module applies aggregators to consolidate the inner products or superposition segments into a distribution function representing an attribute over a domain reflecting a selected weight table, aggregator, and event type, corresponding to each value of the attribute.

Abstract: A signal interpretation engine apparatus and method are disclosed in certain presently preferred embodiment as including a computer programmed to run a plurality of modules comprising a feature expansion module, a weight table module, a consolidation module, and a map generation module. The feature expansion module contains feature operators for operating on a signal to expand the signal to form a feature map of feature segments Each feature segment corresponds to a unique representation of the signal created by a feature operator operating on the signal across an epoch. An epoch corresponds to an event occurring within a time segment. The weight table module provides a weight table having weight elements Each weight element has a weight corresponding to a feature segment of the feature map. The consolidation module provides a superposition segment by combining the feature segments of the feature map corresponding to the epoch by forming an inner product of the feature map and the weight table.