A system and method is provided for modeling brain dynamics in normal and diseased states.

A system and method is provided for modeling brain dynamics in normal and diseased states.

System and methods for stimulating the neurovascular system of the cerebral tissue through optimally placed devices, while simultaneously measuring the evoked neuronal and hemodynamic responses, also using optimally placed devices, is disclosed. Systems and methods for iteratively stimulating the neurovascular system and recording neuronal and hemodynamic responses are also disclosed. Further, a method for determining cerebral neurovascular functioning from the combined stimulation and measurement is disclosed, for use in diagnosis of neurovascular disorders.

An implantable device for implantation in a human body or animal body. The device includes an energy source, an energy storage device, and an electronics unit. Further, an actuator is coupled with the energy storage device and it is configured to emit electromagnetic waves by discharging the energy storage device.

A decision support tool for development of drugs targeting central nervous system conditions. The tool receives measurements made on subjects, which are converted to model outputs using neurocircuitry models. The models are used by a computing device to generate neuro-circuitry based signatures. Neuro-circuitry based signatures associated with an investigational compound may be compared to reference neuro-circuitry based signatures to identify parameters of a clinical trial protocol. The neuro-circuitry based signature comparisons, when generated based on measurement data collected in early phases of a clinical trial process, may increase the likelihood that the investigational compound will quickly and cost-effectively emerge from clinical trials with proof that the investigational compound is effective for treating one or more CNS conditions. The decision support tool may also indicate early phase measurements to make based on a condition against which an investigational compound is theorized to be effective against.

Provided herein are methods to predict pain sensitivity and pain intensity to prolonged pain in a subject. Electroencephalograms are recorded in a pain-free state or, alternatively, in a pain-free state and after applying a prolonged pain stimulus in a prolonged pain state. Pain-free and prolonged pain peak alpha frequencies or ΔPAF are measured. These values correlate negatively with the likelihood of increased pain sensitivity and increased pain intensity. Also provided is a method for predicting a likelihood of chronic pain in a subject after a medical procedure and designing a plan to treat the chronic pain.

Described is a computer system for establishing an electroencephalogram (EEG) model for discriminating between alert and fatigue states. The computer system comprises a receiver module for receiving an alert state segment illustrative of an alert state of at least one subject, and one or more EEG fatigue data segments illustrative of a fatigue state of the at least one subject. The computer system further comprises a segment selector for selecting one of the one or more fatigue data segments and setting it to be an assumed maximum fatigue segment, an EEG classifier trainer for training an EEG classifier by extracting an EEG feature space from the alert state segment and assumed maximum fatigue segment, and a maximum fatigue identifier module for identifying a segment of maximum fatigue by applying the EEG classifier to each of the fatigue data segments. The computer system further comprises a segment comparator for determining if the segment of maximum fatigue is consistent with the assumed maximum fatigue segment, and a limit setter for setting the segment of maximum fatigue as a revised assumed maximum fatigue segment, if the segment of maximum fatigue is inconsistent with the assumed maximum fatigue segment, and supplying the EEG classifier trainer with the revised assumed maximum fatigue segment. The computer system further comprises a model output module for setting the EEG classifier as the EEG model for discriminating between alert and fatigue states in segments of EEG data, if the segment of maximum fatigue is consistent with the assumed maximum fatigue segment.

Described is a computer system for establishing an electroencephalogram (EEG) model for discriminating between alert and fatigue states. The computer system comprises a receiver module for receiving an alert state segment illustrative of an alert state of at least one subject, and one or more EEG fatigue data segments illustrative of a fatigue state of the at least one subject. The computer system further comprises a segment selector for selecting one of the one or more fatigue data segments and setting it to be an assumed maximum fatigue segment, an EEG classifier trainer for training an EEG classifier by extracting an EEG feature space from the alert state segment and assumed maximum fatigue segment, and a maximum fatigue identifier module for identifying a segment of maximum fatigue by applying the EEG classifier to each of the fatigue data segments. The computer system further comprises a segment comparator for determining if the segment of maximum fatigue is consistent with the assumed maximum fatigue segment, and a limit setter for setting the segment of maximum fatigue as a revised assumed maximum fatigue segment, if the segment of maximum fatigue is inconsistent with the assumed maximum fatigue segment, and supplying the EEG classifier trainer with the revised assumed maximum fatigue segment. The computer system further comprises a model output module for setting the EEG classifier as the EEG model for discriminating between alert and fatigue states in segments of EEG data, if the segment of maximum fatigue is consistent with the assumed maximum fatigue segment.

An embodiment of the invention provides a wireless in-ear utility device that rests in the user's ear canal near the user's eardrum. The in-ear utility device may be configured in a variety of ways, including, but in no way limited to a smart in-ear utility device, a flexible personal sound amplification product, a personal music player, a “walkie-talkie” and the like.