Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.

A method for functional brain mapping using high gamma modulation obtained from stereoelectroencephalography (SEEG).

A method for functional brain mapping using high gamma modulation obtained from stereoelectroencephalography (SEEG).

An example system to identify an advertisement to include in source material to increase an effectiveness of the advertisement includes an analyzer to determine one or more priming characteristics for a plurality of locations of a source material based on neuro-response data collected from a first subject exposed to the source material and a selector to identify an attribute of the advertisement, identify at least one of a temporal attribute or a spatial attribute for the plurality of locations, perform a comparison of the attribute of the advertisement to the at least one of the temporal attribute or the spatial attribute for the plurality of locations, select a first location of the plurality of locations for insertion of the advertisement based on the comparison and the priming characteristics, and transform the source material to include the advertisement at the first location.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

One embodiment provides a method, including: obtaining EEG data from one or more single channel EEG sensor worn by a user; classifying, using a processor, the EEG data as one of nominal and abnormal; and providing an indication associated with a classification of the EEG data. Other embodiments are described and claimed.

The present disclosure pertains to a system configured to enhance deep sleep in a subject during positive airway pressure therapy. A pressure generator is configured to generate a pressurized flow of breathable gas for delivery to an airway of the subject. Sensors are configured to generate output signals conveying information related to breathing of the subject. One or more hardware processors are configured to cause the pressure generator to generate the pressurized flow of breathable gas in accordance with a positive airway pressure therapy regime based on the information in the output signals; determine sleep stages of the subject based on the information in the output signals; and responsive to the sleep stages indicating the subject is in deep sleep, cause the pressure generator to adjust the pressurized flow of breathable gas to deliver a stimulus to the subject, the stimulus configured to enhance deep sleep in the subject.

A computer-implemented method for determining the state of a system, which includes steps of: collecting data relating to a system, the data being noisy data comprising data of interest and noise; generating a signal to be analyzed from the collected data, the signal being a noisy signal comprising a signal of interest and noise; analyzing the regularity of the signal of interest by compensating the influence of the noise in the computation of the power of the difference between the integrated noisy signal and its trend; and determining the state of the system depending on the result of the analysis of the regularity of the signal of interest.

An apparatus, system and technique selectively eliminates the noxious signal components in a neuronal signal by creating an interfering electrical signal that is tuned to a given frequency corresponding to the oscillatory pattern of the noxious signal, resulting in a modified neuronal signal that substantially reproduces a normal, no-pain neuronal signal. The disclosed system and technique of pain relief is based on the hypothesis that the temporal profile of pain signals encodes particular components that oscillate at unique and quantifiable frequencies, which are responsible for pain processing in the brain.

An apparatus, system and technique selectively eliminates the noxious signal components in a neuronal signal by creating an interfering electrical signal that is tuned to a given frequency corresponding to the oscillatory pattern of the noxious signal, resulting in a modified neuronal signal that substantially reproduces a normal, no-pain neuronal signal. The disclosed system and technique of pain relief is based on the hypothesis that the temporal profile of pain signals encodes particular components that oscillate at unique and quantifiable frequencies, which are responsible for pain processing in the brain.