Abstract: A motor teaching system is provided with a force presentation robot for teaching motion in accordance with a predetermined motor pattern by guiding movement of a movable part of the body of a test subject, a brainwave sensor, and a computer that classifies a cerebral activity pattern into one of a plurality of classes that include motor imagery, based on a brainwave signal. The computer guides evoking contents of the test subject prior to teaching of the motor pattern, such that a classification result obtained in the period of teaching the motor pattern will be the motor imagery class.

Abstract: A modularized externally-driven joint structure that can be used for general purposes. In one aspect, an externally-driven joint structure includes: a shaft member that extends in an axial direction; and a plurality of rotatable members that are arranged along the axial direction, and are coupled with each other by the shaft member in an axially rotatable manner. Each of the rotatable members includes a pair of face portions that face each other in the axial direction, a side wall portion that is arranged along the outer circumferential edges of the pair of face portions, and at least one coupling portion that is arranged at the face portions or the side wall portion, and is coupled with a link member constituting a link of a robot.

Abstract: An estimation system obtains brain wave measurement data and functional magnetic resonance imaging measurement data simultaneously measured from a subject, calculates first functional connectivity for each channel combination based on correlation between channels included in the brain wave measurement data, calculates second functional connectivity for each brain network based on correlation between regions of interest included in the functional magnetic resonance imaging measurement data, calculates a disorder-likelihood label by calculating a score representing disorder-likelihood to be estimated with the use of a plurality of second functional connectivities, and determines an estimation model for estimating disorder-likelihood based on prescribed first functional connectivity by machine learning using the first functional connectivity for each channel combination and the disorder-likelihood label.

Abstract: There is provided a therapy selection support device that generates a discriminator (identifier) as a diagnostic marker or a classifier as a stratification marker through machine learning on the basis of measurement data on brain activities and that uses the discriminator or the classifier as a biomarker. A therapy selection support system 300a, 300b, 500 includes a clustering device 300b that executes stratification in which the results of measurement of brain functional connectivity correlation values acquired from a plurality of second subjects are divided into a plurality of clusters through a clustering process.

Abstract: [Object] To provide a compact, high-output actuator device allowing force control. [Solution] An actuator device 1000 includes an electromagnetic coil member 110 provided over a prescribed width on an outer circumference of a cylinder 100, and a movable element 200 slidable as a piston in the cylinder 100. The movable element 200 has a magnetic member 202, and is moved relatively by excitation of the electromagnetic coil member 110. Fluid is supplied to first and second chambers 106a and 106b such that when the movable element 200 is to be moved relatively, the movable element 200 is driven in the same direction.

Abstract: A brain functional connectivity correlation value clustering device for clustering subjects having a prescribed attribute on the basis of brain measurement data obtained from a plurality of facilities, wherein a plurality of MRI devices capture resting state fMRI image data of a healthy cohort and a patient cohort; a computing system 300 performs generation of an identifier as ensemble learning of “supervised learning” between harmonized component values of correlation matrixes and disease labels of each of the subjects, selects, during the ensemble learning, features for clustering in accordance with importance from the features specified for generating an identifier for a disease label, and performs multiple co-clustering by “unsupervised learning.

Abstract: A rotational structure is configured such that a hollow portion, in which a base member is opposed to a rotational member, is formed around a shaft member. An encoder provided in the hollow portion includes a detection target member rotated together with one of the rotational member and the base member and having a physical quantity changing in a circumferential direction, and a detector capable of detecting the physical quantity of the detection target element and rotated together with the other of the rotational member and the base member.

Abstract: [Object] An object is to provide a brain activity analyzing method for realizing a biomarker using brain function imaging for neurological/mental disorders. [Solution] From data of resting-state functional connectivity MRI obtained by measuring groups of healthy subjects and patients, a correlation matrix of degrees of activities among prescribed brain regions is derived. By a sparse canonical correlation analysis (SCCA) of attributes of subjects and the correlation matrix, elements of correlation matrix that connect to canonical variables corresponding only to the diagnosis label are extracted. By sparse logistic regression (SLR) during Leave-One-Out Cross Validation of a first sum-set of elements of correlation matrix obtained as a result of feature extraction by a sparse regularized canonical correlation analysis, a second sum-set of elements of the correlation matrix is extracted. By discrimination analysis using sparse logistic regression on the second sum-set, a discriminator is generated.

Abstract: A modularized externally-driven joint structure can be used for general purposes. An externally-driven joint structure: includes a shaft member that extends in an axial direction; and a number of rotatable members that are arranged along the axial direction, and are coupled with each other by the shaft member in an axially rotatable manner. Each of the rotatable members includes a pair of face portions that face each other in the axial direction, a side wall portion that is arranged along the outer circumferential edges of the pair of face portions, and at least one coupling portion that is arranged at the face portions or the side wall portion, and is coupled with a link member constituting a link of a robot.

Abstract: [Object] To provide a compact, high-output actuator device allowing force control. [Solution] An actuator device 1000 includes an electromagnetic coil member 110 provided over a prescribed width on an outer circumference of a cylinder 100, and a movable element 200 slidable as a piston in the cylinder 100. The movable element 200 has a magnetic member 202, and is moved relatively by excitation of the electromagnetic coil member 110. Fluid is supplied to first and second chambers 106a and 106b such that when the movable element 200 is to be moved relatively, the movable element 200 is driven in the same direction.

Abstract: [Object] To provide a compact, high-output actuator device allowing force control. [Solution] An actuator device 1000 includes an electromagnetic coil member 110 provided over a prescribed width on an outer circumference of a cylinder 100, and a movable element 200 slidable as a piston in the cylinder 100. The movable element 200 has a magnetic member 202, and is moved relatively by excitation of the electromagnetic coil member 110. Fluid is supplied to first and second chambers 106a and 106b such that when the movable element 200 is to be moved relatively, the movable element 200 is driven in the same direction.

Abstract: A harmonization system for a brain activity classifier harmonizing brain measurement data obtained at a plurality of sites to realize a discrimination process based on brain functional imaging: obtains data, for a plurality of traveling subjects as common objects of measurements at each of the plurality of measurement sites, resulting from measurements of brain activities of a predetermined plurality of brain regions of each of the traveling subjects; calculates, for each of the traveling subjects, prescribed elements of a brain functional connectivity matrix representing the temporal correlation of brain activities of a set of the plurality of brain regions; using a generalized linear mixed model, calculates measurement bias data 3108 for each element of the brain functional connectivity matrix, as a fixed effect at each measurement site with respect to an average of the corresponding element across the plurality of measurement sites and across the plurality of traveling subjects; and thereby executes a harm

Abstract: A training apparatus 1000 using a method of decoding nerve activity includes: a brain activity detecting device 108 for detecting brain activity at a prescribed area within a brain of a subject; and an output device 130 for presenting neurofeedback information (presentation information) to the subject. A processing device 102 decodes a pattern of cranial nerve activity, generates a reward value based on a degree of similarity of the decoded pattern with respect to a target activation pattern obtained in advance for the event as the object of training, and generates presentation information corresponding to the reward value.

Abstract: Objective discrimination of a disease label of a depressive symptom with respect to an active state of a brain is achieved. One means for solving the problems of the present invention is to provide a discriminating device for assisting in determination of whether a subject has a depressive symptom. The discriminating device includes a storage device for storing information for identifying a classifier generated by classifier generation processing based on a signal obtained by using a brain activity detecting apparatus to measure, in advance and time-sequentially, a signal indicating a brain activity of a plurality of predetermined regions of each brain of a plurality of participants in a resting state, the plurality of participants including healthy individuals and patients with depression.

Abstract: A brain network activity estimation system includes means for obtaining brain wave measurement data and functional magnetic resonance imaging measurement data simultaneously measured from a subject, means for constructing a feature estimation model which receives the brain wave measurement data as input data and determining a parameter which defines the feature estimation model, means for calculating a feature value for each module based on an output value from each module that is calculated when the brain wave measurement data is provided as the input data, means for calculating an image feature value for each brain network based on the functional magnetic resonance imaging measurement data, and means for determining one or more modules which express activities of a specific brain network among a plurality of modules by evaluating correlation between the feature value for each module and the image feature value for each brain network.

Abstract: [Object] To provide a hybrid actuator attaining both driving force and responsiveness, capable of reducing inertia of a movable portion. [Solution] A pneumatic air muscle has a cylinder (112) provided in a flexible member (100) forming a pneumatic artificial muscle. At the center of an upper lid element (109) of the cylinder, a through hole is opened, and an inner wire (103) of a Bowden cable passes through this through hole and is coupled by means of a spring (106) to a bottom portion of the cylinder. When the pneumatic artificial muscle contracts, the inner wire (103) and the pneumatic air muscle move together because of the stopper (105), and the contraction force is transmitted. In contrast, when the pneumatic air muscle extends, the stopper (105) is disengaged, while the tension of inner wire (103) is kept by the spring (106) to prevent slacking.

Abstract: Neurofeedback training executed by a brain activity training apparatus for performing a training to change the correlation of connectivity between brain areas utilizing correlation of measured connectivity between brain areas involves repetition of a plurality of trials. Each trial includes a resting period Trest, a self-regulation period TNF and a presenting period TScore presenting feedback information. The brain activity training apparatus calculates, from signals detected from a trainee in the resting period by a brain activity detecting device, a baseline level of degrees of activity of prescribed regions corresponding to the functional connectivity as the object of training, calculates, from signals detected in the self-regulation period and from the baseline level, time correlation of degrees of activity of the prescribed regions corresponding to the functional connectivity as the object of training, and calculates information to be fedback.

Abstract: An optical sensor includes a light emitter configured to irradiate a surface of an object with a plurality of non-parallel light beams, a light detector configured to detect a plurality of light beams that have been reflected within the object and have returned to the surface from a plurality of directions, a recording unit configured to store pre-calculated results of a plurality of models having different optical properties and physical structures, and a calculating unit configured to calculate a light amount ratio of the plurality of reflected light beams, and estimate an optical property of the object based on the calculated light amount ratio and the pre-calculated results.

Abstract: [Object] An object is to provide a brain activity measuring apparatus and a brain activity measuring method that can precisely ensure reproducibility of measurements for a plurality of measurements made separately for a number of times using functional magnetic resonance imaging. [Solution] In an MRI apparatus 10, a data processing unit 32 determines rigid transformation between an original tomographic image obtained in a past measurement and stored in a storage unit 36 and a pilot tomographic image obtained in a present imaging session such that mutual information between the original and pilot tomographic images is locally maximized; and based on a parameter of the determined rigid transformation, position and direction of a slice image of the tomographic image in the present imaging session are corrected.

Abstract: [Object] An object is to provide a brain activity analyzing method for realizing a biomarker using brain function imaging for neurological/mental disorders. [Solution] From data of resting-state functional connectivity MRI obtained by measuring groups of healthy subjects and patients, a correlation matrix of degrees of activities among prescribed brain regions is derived. By a sparse canonical correlation analysis (SCCA) of attributes of subjects and the correlation matrix, elements of correlation matrix that connect to canonical variables corresponding only to the diagnosis label are extracted. By sparse logistic regression (SLR) during Leave-One-Out Cross Validation of a first sum-set of elements of correlation matrix obtained as a result of feature extraction by a sparse regularized canonical correlation analysis, a second sum-set of elements of the correlation matrix is extracted. By discrimination analysis using sparse logistic regression on the second sum-set, a discriminator is generated.