A method for generating a transcranial magnetic stimulation (TMS) coil pose atlas based on electromagnetic simulating calculation includes: constructing coil array positions and orientations of a scalp in a standard Montreal Neurological Institute (MNI) space and matching the coil array positions and orientations of the scalp to a brain space of an individual to obtain coil array positions and orientations of the brain space of the individual; using a finite element calculation method to simulate the coil array positions of the brain space of the individual to obtain induced electric field distributions of brain tissue in different coil orientations; obtaining optimal regulation effects based on the induced electric field distributions of the brain tissue; and obtaining a coil position and orientation corresponding to each optimal regulation effect as an optimal coil pose of each divided brain area of the individual, and constructing a TMS coil pose atlas of the individual.

A system for monitoring and responding to a cerebrovascular accident includes a computer, including a computer processor and a computer transceiver, a storage that stores personal medical information and a risk level, and a portable device attached to a user. When the computer transceiver receives personal medical information of the user, the computer processor calculates the risk level of a cerebrovascular accident based on the personal medical information. When the computer transceiver receives acceleration information, the computer processor determines whether to contact a predetermined contact based on the risk level and the personal medical information. The portable device includes an acceleration sensor, a position receiver, a clock, a portable transceiver, and a portable processor.

A device for measuring and/or stimulating a brain activity, preferably an EEG device, including a transmitter and/or detector for transmitting and/or detecting physiological signals produced by the brain of an individual, and a support for the transmitter and/or detector, wherein the support is configured to extend over the top of the individual's head, the support removably attachable to an accessory intended to be worn by the individual, on his or her head, such as an audio headset, the support being configured such that, when the device is worn by the individual, the transmitter and/or detector are held in substantially close contact with the individual's head by the accessory.

According to one aspect of the present disclosure, there is provided a method of estimating biometric information about a head using a machine learning, the method including: acquiring an analysis target optical signal detected from a head portion of a person to be measured by at least one optical sensor disposed on the head portion of the person to be measured; and estimating the biometric information about the head of the person to be measured by analyzing the acquired analysis target optical signal using an estimation model learned based on data about an anatomical structure of at least one head portion, data about a biometrical state of the at least one head portion, and data about an optical signal associated with the data about the anatomical structure of at least one head portion and the data about the biometrical state of the at least one head portion.

A cognition evaluation method, comprises: obtaining brain physiology information associated with a subject by a brain state measuring instrument, obtaining a plurality of cognitive aspects according to the brain physiology information, selecting at least one of the cognitive aspects according to a sport type as at least one sport cognitive aspect, obtaining a cognition task outcome corresponding to the at least one sport cognitive aspect, and evaluating and outputting a sport cognition level of the at least one sport cognitive aspect according to the cognition task outcome. The present disclosure further provides a cognition evaluation system.

A multi-modal biomarker predictive of a pain level in a patient that includes at least one of a structural MRI-based parameter and a functional MRI-based parameter from the brain of the patient is described. Systems and computer-implemented methods of estimating a pain level in a patient based that transform the multimodal into the estimated pain level using a machine learning model are also disclosed.

The present disclosure provides methods and systems for high-resolution functional magnetic resonance imaging (fMRI), including real-time high-resolution functional MRI methods and systems.

A determination result output device and a determination result output system output a determination result indicating various states of a subject, and a determination result provision device provides a determination result. A determination result output system includes a determination result output device that outputs a determination result indicating the state of a subject, and a determination result provision device that provides the determination result. When acquiring facial-surface change information related to a target purpose from the determination result output device, the determination result provision device generates determination information from facial-surface change information, determines a determination result including a state level of the subject, and sends the determination result to the determination result output device.

A method for measuring an intracranial bioimpedance in a patient's head, to help evaluate cerebral autoregulation, may involve securing a volumetric integral phase-shift spectroscopy (VIPS) device to the patient's head, measuring the intracranial bioimpedance with the VIPS device by measuring a phase shift between a magnetic field transmitted from a transmitter on one side of a VIPS device and a magnetic field received at a receiver on another side of the VIPS device, at one or more frequencies, and evaluating cerebral autoregulation in the intracranial bioimpedance, using a processor in the VIPS device.

The present disclosure relates to a brain-computer interface system and method. In an example, a brain-computer interface system includes a data processing unit, a data transceiver unit, and a sensing or stimulation unit. The system also includes a first communication path between the data transceiver unit and the sensing or stimulation unit including a first downlink channel for transmitting power and data from the data transceiver unit to the data sensing unit and a first uplink channel for transmitting data from the sensing or stimulation unit to the data transceiver unit. The system may additionally include a second communication path between the data processing unit and the data transceiver unit including a second downlink channel for transmitting power and data from the data processing unit to the data transceiver unit and a second uplink channel for transmitting data from the data transceiver unit to the data processing unit.