A system for performing functional brain mapping includes a memory configured to store first data from a magnetic resonance imaging (MRI) system and second data from electrodes. The system also includes a processor operatively coupled to the memory and configured to identify first edges in a brain network based on the first data from the MRI and second edges in the brain network based on the second data from the electrodes. The processor is configured to determine, based on the first edges and the second edges, connectivity metrics for the brain network. The processor is also configured to generate, based at least in part on the connectivity metrics, a decoder that differentiates between critical nodes and non-critical nodes in the brain network.

A system for performing functional brain mapping includes a memory configured to store first data from a magnetic resonance imaging (MRI) system and second data from electrodes. The system also includes a processor operatively coupled to the memory and configured to identify first edges in a brain network based on the first data from the MRI and second edges in the brain network based on the second data from the electrodes. The processor is configured to determine, based on the first edges and the second edges, connectivity metrics for the brain network. The processor is also configured to generate, based at least in part on the connectivity metrics, a decoder that differentiates between critical nodes and non-critical nodes in the brain network.

In each trial, brain electrical activity at multiple points of a target person is measured. An acquirer of a determination device acquires response matrices for n trials under a first condition and response matrices form trials under a second condition. An analyzer performs canonical correlation analysis on the acquired response matrices to obtain first canonical variable time series. A distance calculator calculates a distance between the trials from the obtained first canonical variable time series to obtain a distance matrix. A determiner obtains a possibility that the n trials and the m trials are classified into two different clusters from the distance matrix and determines whether the first condition and the second condition are substantially different. It is possible to provide to a single target person a first content in n trials and a second content in m trials so as to determine a difference in interest of the single target person. It is possible to provide the same content to a first subject who is the target person in n trials and to a second subject who is the target person in m trials so as to determine whether the two are different or the same.

In each trial, brain electrical activity at multiple points of a target person is measured. An acquirer of a determination device acquires response matrices for n trials under a first condition and response matrices form trials under a second condition. An analyzer performs canonical correlation analysis on the acquired response matrices to obtain first canonical variable time series. A distance calculator calculates a distance between the trials from the obtained first canonical variable time series to obtain a distance matrix. A determiner obtains a possibility that the n trials and the m trials are classified into two different clusters from the distance matrix and determines whether the first condition and the second condition are substantially different. It is possible to provide to a single target person a first content in n trials and a second content in m trials so as to determine a difference in interest of the single target person. It is possible to provide the same content to a first subject who is the target person in n trials and to a second subject who is the target person in m trials so as to determine whether the two are different or the same.

A method for administering stimulations to a sleeping subject is provided. The method includes obtaining brain wave data generated based on brain wave activity of the subject over a predetermined time frame and determining a spectral power ratio of a spindle band to delta and theta bands of the brain wave data at a time within the predetermined time frame. The spectral power ratio and brain wave data are sent to the input of a pretrained deep neural network to generate a probability score that sleep spindles are being detected in the brain wave activity. The method may continue to obtain brain wave data and analyze the data using the pretrained deep neural network. A determination that sleep spindles are detected may be made when the probability score is above a predetermined threshold score for a predetermined threshold period of time.

Systems, devices, and methods described herein relate to multi-sensory presentation devices, including virtual reality (VR) devices, visual display devices, sound devices, haptic devices, and other forms of presentation devices, that are configured to present sensory elements, including visual and/or audio scenes, to a user. In some embodiments, one or more sensors including electroencephalography (EEG) sensors and a photoplethysmography (PPG) sensors, e.g., included in a brain-computer interface, can measure physiological data of a user to monitor a state of the user during the presentation of the visual and/or audio scenes. Such systems, devices, and methods can adapt one or more visual and/or audio scenes based on user physiological data, e.g., to control or manage the state of the user.

Systems, devices, and methods described herein relate to multi-sensory presentation devices, including virtual reality (VR) devices, visual display devices, sound devices, haptic devices, and other forms of presentation devices, that are configured to present sensory elements, including visual and/or audio scenes, to a user. In some embodiments, one or more sensors including electroencephalography (EEG) sensors and a photoplethysmography (PPG) sensors, e.g., included in a brain-computer interface, can measure physiological data of a user to monitor a state of the user during the presentation of the visual and/or audio scenes. Such systems, devices, and methods can adapt one or more visual and/or audio scenes based on user physiological data, e.g., to control or manage the state of the user.

An information processor according to an aspect of the present disclosure includes a deriving unit that derives a cognitive capacity of a user on a basis of dispersion of reaction times of the user for a plurality of requests.

The present application provides for use of a somatosensory evoked potential (SEP) during the hyperacute stroke phase as marker for predicting the outcome of endovascular treatment in a patient suffering from acute ischemic stroke, wherein when the SEP ipsilateral to the stroke site has an amplitude from 60% to 100% with respect to the corresponding SEP contralateral to the stroke site this is indicative of good outcome of the endovascular treatment, whereas when the SEP ipsilateral to the stroke site has an amplitude from 0% to 20% with respect to the corresponding SEP contralateral to the stroke site this is indicative of bad outcome of the endovascular treatment.

Provided herein are methods for analyzing in vivo a brain circuit. A method of the present disclosure may include using optogenetics to stimulate a first region of a brain of an individual, in conjunction with functional magnetic resonance imaging (fMRI) of different regions of the brain to determine a dynamic functional connection between individual neurons of the first region and a second region of the brain. The method may further include identifying a third region of the brain, the neurons of which region mediate the dynamic functional connection between the first and second regions.