A device and a signal processing method that can be used with a device to recognize and distinguish a physiological high-frequency oscillation (HFO) from a pathological high-frequency oscillation. The signal processing method detects a physiological HFO in the electrical brain signal one regimen of electrical or optogenetic brain stimulation can be triggered, alternatively if the method detects a pathological HFO associated with epilepsy a different regimen of electrical or optogenetic brain stimulation can be triggered. Thus, the signal processing method can be utilized in a closed loop brain stimulation device that serves the dual purpose of both enhancing memory encoding, consolidation, and recall, or improving cognition, and reducing the probability of a seizure in a patient with epilepsy.

A device and a signal processing method that can be used with a device to recognize and distinguish a physiological high-frequency oscillation (HFO) from a pathological high-frequency oscillation. The signal processing method detects a physiological HFO in the electrical brain signal one regimen of electrical or optogenetic brain stimulation can be triggered, alternatively if the method detects a pathological HFO associated with epilepsy a different regimen of electrical or optogenetic brain stimulation can be triggered. Thus, the signal processing method can be utilized in a closed loop brain stimulation device that serves the dual purpose of both enhancing memory encoding, consolidation, and recall, or improving cognition, and reducing the probability of a seizure in a patient with epilepsy.

A content presentation system, a content presentation device, and a content presentation method that reduce a burden on a user and present suitable contents to the user with high accuracy are provided. The present technology provides a content presentation system including a computer device that holds content information associated with emotion information indicating an emotion of a user, in which the computer device at least includes a machine learning model that, on the basis of a plurality of pieces of content information presented to the user corresponding to desired emotion information indicating emotion information desired by the user and content information selected by the user from the plurality of pieces of content information, performs machine learning so as to present the content information suitable for the emotion information.

An intra-calvarial implant (ICI) includes a housing including a sealed compartment having a top part, a bottom part and a side wall, and a current directing mechanism extending from the bottom part of the sealed compartment. The ICI also includes one or more electrodes for sensing electrical signals from the brain and/or for electrically stimulating one or more regions of the brain. The ICI includes at least one auxiliary electrode (that may be a reference and/or source/sink electrode) and an electronic circuitry module (ECM), sealingly disposed within the sealed compartment and operatively connected to the one or more electrodes and to the at least one reference electrode. The ECM controls the operation of the ICI and wirelessly communicates with an external telemetry device. The ICI includes a power harvesting device electrically connected to the ECM of the ICI for providing power thereto.

An intra-calvarial implant (ICI) includes a housing including a sealed compartment having a top part, a bottom part and a side wall, and a current directing mechanism extending from the bottom part of the sealed compartment. The ICI also includes one or more electrodes for sensing electrical signals from the brain and/or for electrically stimulating one or more regions of the brain. The ICI includes at least one auxiliary electrode (that may be a reference and/or source/sink electrode) and an electronic circuitry module (ECM), sealingly disposed within the sealed compartment and operatively connected to the one or more electrodes and to the at least one reference electrode. The ECM controls the operation of the ICI and wirelessly communicates with an external telemetry device. The ICI includes a power harvesting device electrically connected to the ECM of the ICI for providing power thereto.

A rehabilitation support system includes: a sensor data acquirer that acquires sensor data that includes biometric information of a user measured by a sensor; a state calculator that obtains a state of the user based on the sensor data thus acquired; a predictor that predicts the state of the user based on the state of the user obtained by the state calculator; a storage unit that stores support information that is to be presented as information that supports rehabilitation; a selection unit that selects the support information stored in the storage unit, based on the state of the user predicted by the predictor; and a presentation unit that presents the support information selected by the selection unit.

Computing systems and computer-implemented methods for removing brain stimulation artifacts in neural signals are disclosed. The method makes use of a matching pursuit algorithm to accurately extract the stimulation artifact. The disclosed method removes the stimulation artifact associated with individual stimulation pulses without needing additional information from previous stimulation pulses or other electrodes. The disclosed method is compatible for use with various stimulation frequencies, does not need any filtering, and can recover neural signals almost immediately after stimulation. The disclosed method is compatible for use in has great potential in closed-loop systems used in various neurological diagnostic and therapeutic procedures.

A console for medical diagnosis includes a chair, a computer for displaying and communicating test results, various testing areas for performing multiple diagnostic tests, various testing devices including at least an EEG testing device, an ECG testing device, a BMD testing device, an ultrasonography testing device, and an EMG testing device, and openings for kidney probes and an echocardiogram probe. The testing areas include a first area for performing diagnostic tests on the head, a second area for performing diagnostic tests on sensory, a third area for performing diagnostic tests on the chest region, a fourth area for performing diagnostic tests on the pelvic and chest regions, a fifth area for performing diagnostic tests on blood, tissue, and bodily fluids, a sixth area for performing electromyographical tests, a seventh area for performing bone-related diagnostic tests, and an eighth area for performing diagnostic tests related to physical parameters and vitals.

Systems and methods for tracking EEG data and providing enhanced seizure detection and prediction are disclosed. The systems and methods use input sensors for receiving and collecting data from a plurality of EEG channels in association with a subject and processing said data to calculate and average Lyapunov exponents for a composite EEG data set. The systems and methods convert the average Lyapunov exponents into graphical representations that are displayed against a time axis. The graphical output adjusts in real-time according to the input data obtained from EEG channels. The systems and methods utilize pattern recognition to output alarms based upon input data and recommend diagnoses related to seizures.

Systems and methods for tracking EEG data and providing enhanced seizure detection and prediction are disclosed. The systems and methods use input sensors for receiving and collecting data from a plurality of EEG channels in association with a subject and processing said data to calculate and average Lyapunov exponents for a composite EEG data set. The systems and methods convert the average Lyapunov exponents into graphical representations that are displayed against a time axis. The graphical output adjusts in real-time according to the input data obtained from EEG channels. The systems and methods utilize pattern recognition to output alarms based upon input data and recommend diagnoses related to seizures.