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Human beings perform various motion ranging from simple motion to advanced motion. In addition, sensory information is used as a feedback information of the motion state during operations which require fine adjustment. It is necessary for performing advanced motion to acquire the corresponding skills. To learn such skills efficiently, it is necessary to construct a skill learning system. Grasping skill acquisition status is effective for the skill learning system. Currently, there is no other method than evaluating skill acquisition status by using learners’ task scores. However, this method may not be enough to evaluate skill acquisition status. On the other hand, brain has the function of motion commands and sensory acceptance when moving the body. Therefore, it might be possible to sufficiently evaluate skill acquisition status by using the changes of brain activity. In the paper, we measured the changes of cerebral blood flow on learning of force adjustment skill. The obtained data was analyzed by principal component analysis (PCA) and Recurrent Neural Network (RNN). The result of PCA shows that there are some channels which have the changes of the principal component score before and after learning. In the classification by RNN, when the first principal component scores of the cerebral blood flow data before and after learning were used for input data, high classification accuracy was obtained. In addition, higher classification accuracy was obtained when using only channels which have changes in the principal component scores before and after the learning are used as input data, rather than all channels were inputted. From these results, it is shown that characteristics of the prefrontal cerebral blood flow changes according to the motor skill learning.

Distance recognition is the skill to estimate the distance to the target object. Such a distance recognition skill is one of the most important skills in everyday life. In various situations, such as walking, driving a car, communicating with people and so on, humans perceive the distance between the targets, and do the adequate action. Generally, the precision of distance recognition is different according to difference of the ambient surroundings, the objects, and especially the individual’s ability. The distance recognition is achieved by capturing the target object visually and processing its visual information in their brains. Therefore, it is important to investigate how the difference of distance recognition skills affects the brain activity. In the paper, we measure the cerebral blood flow as a brain activity by using NIRS (Near-Infrared Spectroscopy) in the distance recognition.

Human beings perform various motion ranging from simple motion to advanced motion. In addition, sensory information is used as a feedback information of the motion state during operations which require fine adjustment. It is necessary for performing advanced motion to acquire the corresponding skills. In order to learn such skills efficiently, it is necessary to construct a skill learning system. Grasping skill acquisition status is effective for the skill learning system. Currently, there is no other method than evaluating skill acquisition status by using learners’ task scores. However, this method may not be enough to evaluate skill acquisition status. On the other hand, brain has the function of motion commands and sensory acceptance when moving the body. Therefore, it might be possible to sufficiently evaluate skill acquisition status by using the changes of brain activity. In the paper, we measured the changes of EEG accompanying learning on force adjustment skill. The obtained EEG data was filtered to remove some noise using Band-pass filter, and then it was transformed into a frequency component by FFT, and the ratio of α wave and β wave was calculated in the EEG data. In addition, we computed EEG coherence between the electrodes of near the frontal association cortex and the electrodes of near the primary motor cortex, between electrodes of near the frontal association cortex and electrodes of primary somatosensory cortex. As a result, the characteristics of the skill acquisition stage could be confirmed from the change of the ratio of α wave and β wave, increase and decrease of the coherence values.

We proposed operating HMD by BMI to speed up operation and to enhance the convenience in wearable device equipping HMD. But it is difficult to reflect user's thought direct on operation at this stage. In order to realize it, we need to identify a kind of human's brain signal when human thinks about moving object. So our study verifies whether it is possible to identify by imaging direction in data that we investigate cerebral blood flow changes in using NIRS when subject thinks about moving object. Identification was using neural network. The average of identification ratio of 4 direction in Total-Hb, the average of identification ratio of 4 direction in Oxy-Hb, the average of identification ratio of 4 direction in Deoxy-Hb are 32.25%, 36.0% and 37.0%, respectively. The average of identification ratio of 2 direction, up and down, in Total-Hb, the average of identification ratio of 2 direction, up and down, in Oxy-Hb, the average of identification ratio of 2 direction, up and down, in Deoxy-Hb are 63.5%, 62.0% and 62.5%, respectively. The average of identification ratio of 2 direction, left and right, in Total-Hb, the average of identification ratio of 2 direction, left and right, in Oxy-Hb, the average of identification ratio of 2 direction, left and right, in Deoxy-Hb are 58.0%, 55.5% and 69.0%, respectively. From the results, we show the possibility of identification because identification ratio are higher than chance level.

In recent years, measurement techniques and devices of cerebral function and brain activity have been improved. Therefore, “Neuromarketing” has been attracting attention. Neuromarketing is one of commercial activities and is in the marketing researches which study comsumers’ response to purchase activity. Neuromarketing is helpful for companies to elucidate principles of actions unconsciously caused by human and has 2 purposes to increase effectively consumers’ eagerness to buy. First purpose is elucidating what consumers bought for indexes at the time of purchasing decision making. Second purpose is elucidating what consumers regard as important in the indexes. fMRI is used mainly for such a brain activity measurement. However, there are several problems to use fMRI, which has limitation on the posture of the subject is very expensive. Therefore, it is expected that a portable NIRS (Near-infrared spectroscopy) brain measuring apparatus may be suitable for a study of neuromarketing. We investigate the relation between impressions of the product brand and consumers’ brain response. From our investigation, we show that different brand impression results in different effect on the cerebral blood flow by a portable NIRS brain measuring apparatus.

In recent years, Brain-Machine Interface (BMI) has been improved with the rapid development of the cerebral function measurement technologies. BMI is used for measuring the brain activity of the subjects and inferring his/her intention. These measuring results can control the devices such as the electric wheelchair or the electric arm directly. Previous studies have such problems that BMI device is not portable, and it takes much time to attach a lot of sensor nodes on the head of the subject. Therefore, we use portable NIRS, because it is easy for the subjects to mount it and their burden is low. This paper describes the analytical method for cerebral blood flow during imagining affirmative or negative answers to the questions. It was verified whether it is possible to use the NIRS as BMI to discriminate the yes answer or no without voice and gesture. In our study, a subject keeps watching the display which shows a yes-no question, he/she imagines affirmative or negative answer to the questions. In the experiment, one test trial is in 30 seconds and it includes 10 seconds task between 10 seconds rests. Each test set consists of 10 trials. One subject has five test set. In our study, we used Wearable Optical Topography WOT-100 as measurement device of NIRS which has 10 channels (ch7-16) of prefrontal cortex. The NIRS data analysis procedure is as follows; in the first step, we used a band-pass filter to select the data of frequencies from 0.02 Hz up to 0.1 Hz. In the second step, measured NIRS data of each task set is divided into 10 blocks which are included 5 seconds data before the task and 10 seconds data after the task. In the third step, we calculated baseline of measured data from 5 seconds of the beginning and the end of the task blocks, and this baseline fitting is applied to the original data. In the last step, the neural network learned the important elements of the training data and classified the test data. Our method can discriminate between imagining affirmative or negative answers with 70% accuracy. At result, NIRS is useful to discriminate the yes-no answer of the questions.

This paper describes the method for classification of brain state by the measured electroencephalogram (EEG) frequency in directions (up, down, left, and right) imagination. Recently, Brain-Machine Interface (BMI) has been studied in a variety of ways due to the development of brain measurement technology. Therefore, we have used the BMI to identify the human selection of directions. Our method consists of data normalization, principal component analysis and neural network. The maximum value of the identification rate was 46% by using 3 electrodes (F4, F8 and T8) in the previous study. In this study, we improved the learning method of neural network for the improvement of identification rate of brain state. For that purpose, the measurement points of EEG and the number of subjects are increased. As a result, the maximum value of the identification rate was improved.

In recent years, it is difficult to inherit high level sensory skill, because the number of experts is not so much or the experts are too busy to teach their skill to the beginners. Therefore, many learners do the experiential learning through visual and haptic digital teaching materials. In such a system, however, it is difficult to evaluate whether the learner could recognize the sensation and obtain the sensory skill. In the paper, we investigate whether the biological signal such as EEG can be used for the evaluation of the haptic task skill level.