Abstract: A terminal device 2 transmits a wireless frame having a frame length or amplitude that represents the ID of a wireless base station 1 to be woken up to the wireless base station 1. The wireless base station 1 detects the wakeup ID from the frame length or amplitude of the wireless frame from the terminal device 2, and, if the detected wakeup ID is identical with its own ID, transitions from a sleep mode to an operating mode. In this case, the wireless frame is the same wireless frame as that transmitted and received by the wireless communication module of the terminal device 2 to and from the wireless base station 1.

Abstract: In order to solve a problem that a local optical characteristic-changed region inside an object cannot be accurately estimated, an object observing apparatus includes: a light intensity information acquiring unit that acquires light intensity information received by each light-receiving probe; a light intensity change information acquiring unit that acquires, for each probe set, light intensity change information, from reference light intensity information and light intensity information; an estimating unit that acquires three-dimensional optical characteristic-changed region information, using the light intensity change information; and an output unit that outputs the optical characteristic-changed region information; the estimating unit including a correcting part that performs correction according to sensitivity attenuation in accordance with a depth; and a sparseness applying part that introduces sparseness for improving a space resolution, thereby acquiring the optical characteristic-changed region infor

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: Conventionally, it is impossible to detect an object to which a person feels a similar sense based on latent consciousness. Detection of an object to which a person feels a similar sense is realizable by a brain information processing apparatus using high-level brain activity information indicating a latent consciousness. The brain information processing apparatus has stored therein one or more pieces of brain information that includes brain activity information, which is information on a brain activation level, and object information, and includes: an accepting unit that accepts brain activity information; an object information acquisition unit that acquires one or more pieces of object information associated with one or more pieces of brain activity information that is approximate to the brain activity information to the extent of satisfying a predetermined condition; and an output unit that outputs the object information acquired by the object information acquisition unit.

Abstract: A tendency discrimination device includes a discriminator to objectively discriminate a tendency of a person to be tested, based on brain information obtained by magnetic resonance imaging (MRI). In the generation of the discriminator, a gray matter volume and a diffusion anisotropy degree are calculated for a frontal pole of each of multiple test subjects as a region of interest, and machine learning is performed on the relationship of the information obtained by classifying results of a test for discriminating the tendencies of the multiple test subjects, to gray matter volumes and diffusion anisotropy degrees obtained by MRI for each of the multiple test subjects.

Abstract: Provided is a brain activity training apparatus for training to cause a change in correlation of connectivity among brain regions, utilizing measured correlations of connections among brains regions as feedback information. From measured data of resting-state functional connectivity MRI of a healthy group and a patient group (S102), correlation matrix of degree of brain activities among prescribed brain regions is derived for each subject. Feature extraction is executed (S104) by regularized canonical correlation analysis on the correlation matrix and attributes of the subject including a disease/healthy label of the subject. Based on the result of regularized canonical correlation analysis, by discriminant analysis through sparse logistic regression, a discriminator is generated (S108). The brain activity training apparatus feeds back a reward value to the subject based on the result of discriminator on the data of functional connectivity MRI of the subject.

Abstract: Provided is a method of analyzing brain activities for realizing a biomarker for neurological/mental disorder, based on brain function imaging. From measured data of resting-state functional connectivity MRI of a healthy group and a patient group, correlation matrix (80) of degree of brain activities among prescribed brain regions is derived for each subject. Feature extraction is executed by regularized canonical correlation analysis (82) on the correlation matrix (80) and attributes of the subject including a disease/healthy label of the subject. Based on the result of regularized canonical correlation analysis, by discriminant analysis (86) through sparse logistic regression, a discriminator (88) is generated.

Abstract: A transmitter incorporates a table (TBL1) for assigning the frame lengths of 560 ?s and 600 ?s to the code “0” in a binary system and assigning the frame lengths of 600 ?s and 560 ?s to the code “1” in the binary system. The transmitter refers to the table (TBL1) and assigns the two frame lengths of 560 ?s and 600 ?s and the two frame lengths of 600 ?s and 560 ?s to the codes “0” and “1”, respectively, in the code sequences [0,0], [0,1], [1,0] and [1,1] representing the transmission information “0” to “3” represented using the binary system, and sequentially transmits four radio signals having the assigned four frame lengths.

Abstract: What is provides is a brain activity deducing apparatus, a brain activity deducing method, a brain activity measuring apparatus, a brain activity measuring method, and a brain-machine interface device, capable of deducing a brain activity signal of a source subject. The information presentation device presents perceptible information to the first subject. The brain activity measurement device acquires a brain activity signal representing a brain activity of the first subject. The individual conversion device deduces a brain activity signal of the second subject from the acquired brain activity signal based on the individual conversion information, which correlates the brain activity signal of the first subject and the brain activity signal of the second subject.

Abstract: In order to solve a problem that a local optical characteristic-changed region inside an object cannot be accurately estimated, an object observing apparatus includes: a light intensity information acquiring unit that acquires light intensity information received by each light-receiving probe; a light intensity change information acquiring unit that acquires, for each probe set, light intensity change information, from reference light intensity information and light intensity information; an estimating unit that acquires three-dimensional optical characteristic-changed region information, using the light intensity change information; and an output unit that outputs the optical characteristic-changed region information; wherein the estimating unit includes: a correcting part that performs correction according to sensitivity attenuation in accordance with a depth; and a sparseness applying part that introduces sparseness for improving a space resolution, thereby acquiring the optical characteristic-changed regi

Abstract: It is possible to perform robot motor learning in a quick and stable manner using a reinforcement learning apparatus including: a first-type environment parameter obtaining unit that obtains a value of one or more first-type environment parameters; a control parameter value calculation unit that calculates a value of one or more control parameters maximizing a reward by using the value of the one or more first-type environment parameters; a control parameter value output unit that outputs the value of the one or more control parameters to the control object; a second-type environment parameter obtaining unit that obtains a value of one or more second-type environment parameters; a virtual external force calculation unit that calculates the virtual external force by using the value of the one or more second-type environment parameters; and a virtual external force output unit that outputs the virtual external force to the control object.

Abstract: A brain information output apparatus includes an intention determination information storage unit in which two or more pieces of intention determination information can be stored, with each intention determination information including a pair of an intention identifier, and a learning feature amount group including one or more feature amounts extracted from second learning data that is obtained by converting first learning data into intracerebral brain activity data, the first leaning data being acquired from the outside of the cranium of a user when the user performs a trial according to one intention; a first brain activity data acquiring unit that acquires first brain activity data from the outside of the cranium of a user; a second brain activity data acquiring unit that converts the first brain activity data to intracerebral brain activity data, and acquires second brain activity data; a feature amount group acquiring unit that acquires, from the second brain activity data, an input feature amount group

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: When wireless communications are made between a first wireless device and a second wireless device along a route, two wireless devices which exist between the first and second wireless devices each determine the lowest maximum transmission rate (11 Mbps) in the wireless intervals on the route from themselves to the second wireless device to be their own transmission rate txRate (11 Mbps) and two other wireless devices which exist between the first and second wireless devices each determine the lowest maximum transmission rate (54 Mbps) in the wireless intervals on the route from themselves to the second wireless device to be their own transmission rate txRate (54 Mbps).

Abstract: A terminal device 2 transmits a wireless frame having a frame length or amplitude that represents the ID of a wireless base station 1 to be woken up to the wireless base station 1. The wireless base station 1 detects the wakeup ID from the frame length or amplitude of the wireless frame from the terminal device 2, and, if the detected wakeup ID is identical with its own ID, transitions from a sleep mode to an operating mode. In this case, the wireless frame is the same wireless frame as that transmitted and received by the wireless communication module of the terminal device 2 to and from the wireless base station 1.

Abstract: A lexical acquisition apparatus includes: a phoneme recognition section 2 for preparing a phoneme sequence candidate from an inputted speech; a word matching section 3 for preparing a plurality of word sequences based on the phoneme sequence candidate; a discrimination section 4 for selecting, from among a plurality of word sequences, a word sequence having a high likelihood in a recognition result; an acquisition section 5 for acquiring a new word based on the word sequence selected by the discrimination section 4; a teaching word list 4A used to teach a name; and a probability model 4B of the teaching word and an unknown word, wherein the discrimination section 4 calculates, for each word sequence, a first evaluation value showing how much words in the word sequence correspond to teaching words in the list 4A and a second evaluation value showing a probability at which the words in the word sequence are adjacent to one another and selects a word sequence for which a sum of the first evaluation value and the

Abstract: A machine control device includes: first decoders for estimating, from brain signal information, which one of body movements a user performs or images, based on learning using pairs of movements performed by the user and brain signal information, the body movements going toward one of first to nth body postures; second decoders for estimating from the brain signal information, an correct rate on the body movement estimation, based on learning using pairs of correct rates of the estimation of body movements and the brain signal information; and electric prosthetic arm control section for controlling an electric prosthetic arm to change stepwise its posture between first to nth postures via at least one intermediate posture therebetween, the first to nth postures corresponding to the first to nth body postures. The first decoders perform the estimation only when the estimated correct rate exceeds a threshold.

Abstract: A reinforcement learning system (1) of the present invention utilizes a value of a first value gradient function (dV1/dt) in the learning performed by a second learning device (122), namely in evaluating a second reward (r2(t)). The first value gradient function (dV1/dt) is a temporal differential of a first value function (V1) which is defined according to a first reward (r1(t)) obtained from an environment and is served as a learning result given by a first learning device (121). An action policy which should be taken by a robot (R) to execute a task is determined based on the second reward (r2(t)).

Abstract: The present invention provides a motion control system to control a motion of a second motion body, by considering an environment which a human contacts and a motion mode appropriate to the environment, and an environment which a robot actually contacts. The motion mode is learned based on an idea that it is sufficient to learn only a feature part of the motion mode of the human without a necessity to learn the others. Moreover, based on an idea that it is sufficient to reproduce only the feature part of the motion mode of the human without a necessity to reproduce the others, the motion mode of the robot is controlled by using the model obtained from the learning result. Thereby, the motion mode of the robot is controlled by using the motion mode of the human as a prototype without restricting the motion mode thereof more than necessary.

Abstract: What is provides is a brain activity deducing apparatus, a brain activity deducing method, a brain activity measuring apparatus, a brain activity measuring method, and a brain-machine interface device, capable of deducing a brain activity signal of a source subject. The information presentation device presents perceptible information to the first subject. The brain activity measurement device acquires a brain activity signal representing a brain activity of the first subject. The individual conversion device deduces a brain activity signal of the second subject from the acquired brain activity signal based on the individual conversion information, which correlates the brain activity signal of the first subject and the brain activity signal of the second subject.