An apparatus configured to generate an output quality error estimate using a machine-learning error estimation model to compare an output meeting a predetermined quality threshold with an output image reconstructed from a plurality of images, and provide the output quality error estimate for use in estimating if a second subsequent image is required, in addition to a first subsequent image to obtain a cumulative output having an output quality error meeting a predetermined error threshold. Also an apparatus configured, using a received output quality error estimate generated using a machine-learning error estimation model as above, to estimate if a second subsequent image is required, in addition to a first subsequent image, to obtain a cumulative output having an output quality error meeting a predetermined error threshold.

Methods and apparatus configured to allow for users to intentionally interface with an external signal are provided. The methods and apparatus incorporate a randomly-generated electronic signal the behavior of which may be influenced to provide a control output. The methods and apparatus provide a temporal coherence measure influenced by a user that improves the ability to discriminate between intentionality and non-intentionality, and allow for the control of switching, communication, feedback and mechanical movement.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

A method for detecting an epileptic spike includes: obtaining, by a first module of a network model, a local feature of data to be detected, and obtaining, by a second module of the network model, a global feature of the data to be detected; and determining, by a third module of the network model, a detection result of whether there is the epileptic spike in the data to be detected according to the local feature and the global feature. The data to be detected contains a temporal domain and a spatial domain represented by multiple channels, the local feature is a single channel feature, and the global feature is a multichannel feature.

Systems, processes and devices for real-time brain monitoring to generate and control an interface of a display device with a visual representation of a Brain Value Index for entropy, a connectivity map and treatment guidance. Systems, processes and devices for real-time brain monitoring capture sensor data, process the data and dynamically update the interface in real-time.

Systems, processes and devices for real-time brain monitoring to generate and control an interface of a display device with a visual representation of a Brain Value Index for entropy, a connectivity map and treatment guidance. Systems, processes and devices for real-time brain monitoring capture sensor data, process the data and dynamically update the interface in real-time.

The present disclosure provides a non-transitory computer program product embodied in a computer-readable medium and, when executed by one or more analysis modules, providing a visual output for presenting physiological signals of a cardiovascular system. The non-transitory computer program product comprises a first axis representing, subsets of intrinsic mode functions (IMF); a second axis representing a function of signal strength in a time interval; and a plurality of visual elements, each of the visual elements being defined by the first axis and the second axis, and each of the visual elements comprising a plurality of analyzed data units collected over the time interval. Wherein each of the analyzed data units comprises a first coordinate, a second coordinate, and a probability density value generated from an intrinsic probability density function of one of the subsets of IMFs.

The present disclosure provides a non-transitory computer program product embodied in a computer-readable medium and, when executed by one or more analysis modules, providing a visual output for presenting physiological signals of a cardiovascular system. The non-transitory computer program product comprises a first axis representing, subsets of intrinsic mode functions (IMF); a second axis representing a function of signal strength in a time interval; and a plurality of visual elements, each of the visual elements being defined by the first axis and the second axis, and each of the visual elements comprising a plurality of analyzed data units collected over the time interval. Wherein each of the analyzed data units comprises a first coordinate, a second coordinate, and a probability density value generated from an intrinsic probability density function of one of the subsets of IMFs.

A graphene transistor system for measuring electrophysiological signals uses flexible epicortical and intracortical arrays of graphene solution-gated field-effect transistors (gSGFETs) to record infraslow signals alongside signals in the typical local field potential bandwidth. The graphene transistor system includes a processing unit, and at least one graphene transistor (gSGFET) a tunable voltage source connected to the drain and source terminals of the transistor (gSGFET), and at least one filter configured to acquire and split the signal from the transistor into at least a low frequency band signal and high frequency band signal, which are amplifiable with a gain value.