A two part patient monitoring device includes an activator module and a sensor device. The activator module includes a non-galvanic data port that creates a communication path with a non-galvanic data port on the sensor device. The activator module includes power contact pads that are each at least partially surrounded by a bias ring. A bias voltage is applied to the bias rings and a processor or circuit in the activator module monitors the voltage on the bias ring to detect a leakage current. The sensor module includes power contact pins that engage the power contact pads to transfer power from the activator module to the sensor device. Each of the contact pins are surrounded by a seal member such that the connection between the power contact pins and the power contact pads is protected from debris and/or moisture.

A two part patient monitoring device includes an activator module and a sensor device. The activator module includes a non-galvanic data port that creates a communication path with a non-galvanic data port on the sensor device. The activator module includes power contact pads that are each at least partially surrounded by a bias ring. A bias voltage is applied to the bias rings and a processor or circuit in the activator module monitors the voltage on the bias ring to detect a leakage current. The sensor module includes power contact pins that engage the power contact pads to transfer power from the activator module to the sensor device. Each of the contact pins are surrounded by a seal member such that the connection between the power contact pins and the power contact pads is protected from debris and/or moisture.

There is described a computer-implemented method for measurement of a level of pain. The method comprises the steps of: — receiving measured data comprising electroencephalogram, EEG, data collected from one or more EEG electrodes; — extracting from the EEG data, a group 4 indicator corresponding to: — a power, power (ta), associated with a theta-alpha frequency band, ta, within a theta-alpha frequency range; and - determining, based on said group 4 indicator, a level of pain, LoP, which is a value indicative for the level of pain in the subject.

In one aspect, a system for in vivo and transcranial stimulation of brain tissue of a subject may include at least one light source, a controller to control operation of the light source, a signal detecting unit and a processor configured to receive signals from the signal detecting unit, analyze the signals and generate a feedback signal to the controller to control the light source until optimal results are obtained. In one embodiment, the light source is a laser instrument and the wavelength can range from 800 to 1100 nm. In another embodiment, the irradiance of the laser instrument can range from 50 to 1000 mW/cm.sup.2.

Methods, systems, and apparatus for detecting rhythmic synchronization of motor neurons. The system includes one or more sensors for receiving a first signal that measures an electrical signal of one or more neurons, the first signal having a first plurality of specific bursts. The system includes a processor connected to the one or more sensors. The processor is configured to receive the first signal. The processor is configured to generate a second signal based on the first signal using a discrete wavelet transform, the second signal having a second plurality of specific bursts. The processor is configured to determine a time delay between a specific burst within the first plurality of specific bursts and a specific burst within the second plurality of specific bursts using one or more expert rules. The processor is configured to apply the time delay to the first signal.

Methods, systems, and apparatus for detecting rhythmic synchronization of motor neurons. The system includes one or more sensors for receiving a first signal that measures an electrical signal of one or more neurons, the first signal having a first plurality of specific bursts. The system includes a processor connected to the one or more sensors. The processor is configured to receive the first signal. The processor is configured to generate a second signal based on the first signal using a discrete wavelet transform, the second signal having a second plurality of specific bursts. The processor is configured to determine a time delay between a specific burst within the first plurality of specific bursts and a specific burst within the second plurality of specific bursts using one or more expert rules. The processor is configured to apply the time delay to the first signal.

In accordance with some embodiments, systems, apparatus, interfaces, methods, and articles of manufacture are provided for ascertaining aspects of a user, such as the user’s identity, competence, health, and state of mind. In various embodiments, data is captured about a user via a headset worn by the user. Based on the data, a determination may be made about an aspect of the user, and the user may accordingly be granted or denied access to a resource.

In accordance with some embodiments, systems, apparatus, interfaces, methods, and articles of manufacture are provided for ascertaining aspects of a user, such as the user’s identity, competence, health, and state of mind. In various embodiments, data is captured about a user via a headset worn by the user. Based on the data, a determination may be made about an aspect of the user, and the user may accordingly be granted or denied access to a resource.

An estimation system obtains brain wave measurement data and functional magnetic resonance imaging measurement data simultaneously measured from a subject, calculates first functional connectivity for each channel combination based on correlation between channels included in the brain wave measurement data, calculates second functional connectivity for each brain network based on correlation between regions of interest included in the functional magnetic resonance imaging measurement data, calculates a disorder-likelihood label by calculating a score representing disorder-likelihood to be estimated with the use of a plurality of second functional connectivities, and determines an estimation model for estimating disorder-likelihood based on prescribed first functional connectivity by machine learning using the first functional connectivity for each channel combination and the disorder-likelihood label.

An estimation system obtains brain wave measurement data and functional magnetic resonance imaging measurement data simultaneously measured from a subject, calculates first functional connectivity for each channel combination based on correlation between channels included in the brain wave measurement data, calculates second functional connectivity for each brain network based on correlation between regions of interest included in the functional magnetic resonance imaging measurement data, calculates a disorder-likelihood label by calculating a score representing disorder-likelihood to be estimated with the use of a plurality of second functional connectivities, and determines an estimation model for estimating disorder-likelihood based on prescribed first functional connectivity by machine learning using the first functional connectivity for each channel combination and the disorder-likelihood label.