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 module, 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 (IMFs); 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.

The present invention introduces a method, device and a computer program for removing artifacts present in individual channels of a multichannel measurement device. At first, a basis is generated defining an n-dimensional subspace of the N-dimensional signal space, where n is smaller than N, where using in the definition of the n-dimensional basis a physical model of a Signal Space Separation method, or a statistical model based on the statistics of recorded N-dimensional signals. Thereafter, a combined (n+m)-dimensional basis is formed by adding m signal vectors to the n-dimensional basis, each of these m signal vectors representing a signal present only in a single channel of the N-channel device. After this the recorded N-dimensional signal vector is decomposed into n+m components in the combined basis, and finally, components corresponding to the m added vectors in the combined basis are subtracted from the recorded N-dimensional signal vector.

Embodiments of the present systems and methods may relate to a non-invasive system with diagnostic and treatment capacities that use a unified code that is intrinsic to physiological brain function. For example, in an embodiment, a computer-implemented method for affecting living neural tissue may comprise receiving at least one signal from at least one read modality, the signal representing release of photons from mitochondria of the living neural tissue, computing at least one signal to effect alterations to the living neural tissue based on the received input signal, the computed signal adapted to cause transmission of photons to the living neural tissue, and delivering the photons to the living neural tissue to effect alterations to the living tissue.

Embodiments of the present systems and methods may relate to a non-invasive system with diagnostic and treatment capacities that use a unified code that is intrinsic to physiological brain function. For example, in an embodiment, a computer-implemented method for affecting living neural tissue may comprise receiving at least one signal from at least one read modality, the signal representing release of photons from mitochondria of the living neural tissue, computing at least one signal to effect alterations to the living neural tissue based on the received input signal, the computed signal adapted to cause transmission of photons to the living neural tissue, and delivering the photons to the living neural tissue to effect alterations to the living tissue.

Disclosed herein are methods and apparatus for the imaging of brain electrical activity from electromagnetic measurements, using deep learning neural networks where a simulation process is designed to model realistic brain activation and electromagnetic signals to train generalizable neural networks and a residual convolutional neural network and/or a recurrent neural network is trained using the simulated data, capable of estimating source distributions from electromagnetic measurements, and their temporal dynamics over time, for pathological signals in diseased brains, such as interictal activity and ictal signals, and physiological brain signals such as evoked brain responses and spontaneous brain activity.

Disclosed herein are methods and apparatus for the imaging of brain electrical activity from electromagnetic measurements, using deep learning neural networks where a simulation process is designed to model realistic brain activation and electromagnetic signals to train generalizable neural networks and a residual convolutional neural network and/or a recurrent neural network is trained using the simulated data, capable of estimating source distributions from electromagnetic measurements, and their temporal dynamics over time, for pathological signals in diseased brains, such as interictal activity and ictal signals, and physiological brain signals such as evoked brain responses and spontaneous brain activity.

The pulsed pump magnetometer (PPM) is a new type of magnetometer with much higher dynamic range, linearity, and sensitivity than all other types of magnetometers. These features allow more faithful subtracting and cancelling sources of magnetic noise, enabling high quality biomagnetic measurements. Using an array of PPM sensors enables high quality measurements of biomagnetic signals even in magnetically noisy, real-world conditions like medical offices. Arrays of PPM sensors improve upon pulsed magnetic gradiometers in providing higher sensitivity per sensor and superior noise rejection through noise decorrelation and covariance modeling. Arrays of PPM sensors enable localization and imaging of biomagnetic sources.

The pulsed pump magnetometer (PPM) is a new type of magnetometer with much higher dynamic range, linearity, and sensitivity than all other types of magnetometers. These features allow more faithful subtracting and cancelling sources of magnetic noise, enabling high quality biomagnetic measurements. Using an array of PPM sensors enables high quality measurements of biomagnetic signals even in magnetically noisy, real-world conditions like medical offices. Arrays of PPM sensors improve upon pulsed magnetic gradiometers in providing higher sensitivity per sensor and superior noise rejection through noise decorrelation and covariance modeling. Arrays of PPM sensors enable localization and imaging of biomagnetic sources.

A magnetic field generator includes a plurality of conductive windings comprising a first conductive winding arranged in a first plane and a second conductive winding arranged in a second plane that is substantially parallel to the first plane. The plurality of conductive windings are configured to generate, when supplied with a drive current, a first component of a compensation magnetic field. The first component of the compensation magnetic field is configured to actively shield a magnetic field sensing region located between the first conductive winding and the second conductive winding from ambient background magnetic fields along a first axis that is substantially orthogonal to the first plane and the second plane.