Disclosed are methods and devices for modulating delta brainwaves in a sleeping subject. In various embodiments, the method includes a) detecting and monitoring an initial delta wave in a subject, b) applying an electrical current to modulate the initial delta wave in the subject, thereby modifying the initial delta wave in the subject to form a modified delta wave, and, optionally, repeating cycles of steps a) and b), thereby modulating delta brainwaves.

Disclosed are methods and devices for modulating delta brainwaves in a sleeping subject. In various embodiments, the method includes a) detecting and monitoring an initial delta wave in a subject, b) applying an electrical current to modulate the initial delta wave in the subject, thereby modifying the initial delta wave in the subject to form a modified delta wave, and, optionally, repeating cycles of steps a) and b), thereby modulating delta brainwaves.

An intracranial apparatus that may be used for electrophysiological monitoring and stimulation of brain tissue of a patient. Embodiments comprise a stylet including a body section and a tip configured to separate brain tissue, a sheath including a body section having an outer surface defining a central opening configured to receive the stylet such that the tip of the stylet extends beyond the body section of the sheath, and a plurality of electrodes disposed at the outer surface of the sheath and configured to be connected to the brain tissue surrounding the sheath for stimulating and/or monitoring.

An intracranial apparatus that may be used for electrophysiological monitoring and stimulation of brain tissue of a patient. Embodiments comprise a stylet including a body section and a tip configured to separate brain tissue, a sheath including a body section having an outer surface defining a central opening configured to receive the stylet such that the tip of the stylet extends beyond the body section of the sheath, and a plurality of electrodes disposed at the outer surface of the sheath and configured to be connected to the brain tissue surrounding the sheath for stimulating and/or monitoring.

Systems and a computer implemented method for classifying a brain status of a subject, from a neural activity response of the subject to an induced TMS stimulation; the method comprising: constructing a machine learning classifier (MLC) configured to classify a subjects brain status; training the MLC using a training set, the training set comprising pairs of training output-classification vectors and their corresponding training input vectors, all extracted from a database of subjects with known brain status classifications; and applying the trained MLC on an input vector comprising features extracted from a tested-subjects brain neural activity response to the induced TMS stimulation, to obtain an output classification vector for the tested-subjects brain status.

Systems and a computer implemented method for classifying a brain status of a subject, from a neural activity response of the subject to an induced TMS stimulation; the method comprising: constructing a machine learning classifier (MLC) configured to classify a subjects brain status; training the MLC using a training set, the training set comprising pairs of training output-classification vectors and their corresponding training input vectors, all extracted from a database of subjects with known brain status classifications; and applying the trained MLC on an input vector comprising features extracted from a tested-subjects brain neural activity response to the induced TMS stimulation, to obtain an output classification vector for the tested-subjects brain status.

A method for graphically displaying evoked potentials is disclosed herein. The method transforms each of an averaged evoked potentials into a single vertical line, wherein a first amplitude range is represented by a first color, a second amplitude range is represented by a second color, a third amplitude range is represented by a third color and a fourth amplitude range is represented by a fourth color.

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.

An apparatus and method for employing the macro- and micro-structure of sleep and similar states of consciousness to optimize pain-treatment are disclosed wherein an objective biomarker of pain-related sleep disturbance guides pain treatment from a sleep perspective. Furthermore, this concept can be extended to states of reduced consciousness such as coma or sedation. Additionally, it could be applied on individuals who are non-communicative due to injury, disease, language issues and/or infancy.

An apparatus and method for employing the macro- and micro-structure of sleep and similar states of consciousness to optimize pain-treatment are disclosed wherein an objective biomarker of pain-related sleep disturbance guides pain treatment from a sleep perspective. Furthermore, this concept can be extended to states of reduced consciousness such as coma or sedation. Additionally, it could be applied on individuals who are non-communicative due to injury, disease, language issues and/or infancy.