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Background. Dark chocolate has attracted attention for its potential for cognitive improvement. Though some reports indicate that dark chocolate is good for cognitive function, others raise doubts. This inconsistency in past results reflecting the relationship between dark chocolate and cognitive function indicates the potential existence of factors that mediate between dark chocolate intake and cognitive function. Methods. With the hypothesis that fatigue may be one such mediating factor, we performed a four-week randomized control study to seek a link between dark chocolate consumption, cognitive function, fatigue, and the brain in middle-aged adults. Results. We found that dark chocolate reduced mental and physical fatigue, and a path analysis revealed that it enhanced vitality, executive function, memory, and gray matter volume both directly and indirectly. Fatigue reduction was also associated with an improvement in physical function, which had a positive impact on emotional functioning, relief of bodily pain, and social functioning. Conclusions. Our results suggest that dark chocolate may help reduce fatigue in individuals, leading to improvements in brain health and various cognitive functions as well as in quality of life. [ABSTRACT FROM AUTHOR]

Recent advances in brain imaging technology have furthered our knowledge of the neural basis of auditory and speech processing, often via contributions from invasive brain signal recording and stimulation studies conducted intraoperatively. Herein, an approach for synthesizing vowel sounds straightforwardly from scalp‐recorded electroencephalography (EEG), a noninvasive neurophysiological recording method is demonstrated. Given cortical current signals derived from the EEG acquired while human participants listen to and recall (i.e., imagined) two vowels, /a/ and /i/, sound parameters are estimated by a convolutional neural network (CNN). The speech synthesized from the estimated parameters is sufficiently natural to achieve recognition rates >85% during a subsequent sound discrimination task. Notably, the CNN identifies the involvement of the brain areas mediating the "what" auditory stream, namely the superior, middle temporal, and Heschl's gyri, demonstrating the efficacy of the computational method in extracting auditory‐related information from neuroelectrical activity. Differences in cortical sound representation between listening versus recalling are further revealed, such that the fusiform, calcarine, and anterior cingulate gyri contributes during listening, whereas the inferior occipital gyrus is engaged during recollection. The proposed approach can expand the scope of EEG in decoding auditory perception that requires high spatial and temporal resolution. [ABSTRACT FROM AUTHOR]

Error-related brain activation has been investigated for advanced brain-machine interfaces (BMI). However, how a delayed response of cursor control in BMI systems should be handled is not clear. Therefore, the purpose of this study was to investigate how participants responded to delayed cursor control. Six subjects participated in the experiment and performed a wrist-bending task. For three distinct delay intervals (an interval where participants could not perceive the delay, an interval where participants could not be sure whether there was a delay or not, and an interval where participants could perceive the delay), we assessed two types of binary classifications ("Yes + No" vs. "I don't know" and "Yes" vs. "No") based on participants' responses and applied delay times (thus, four types of classification, overall). For most participants, the "Yes vs. No" classification had higher accuracy than "Yes + No" vs. "I don't know" classification. For the "Yes + No" vs. "I don't know" classification, most participants displayed higher accuracy based on response classification than delay classification. Our results demonstrate that a class only for "I don't know" largely contributed to these differences. Many independent components (ICs) that exhibited high accuracy in "Yes + No" vs. "I don't know" response classification were associated with activation of areas from the frontal to parietal lobes, while many ICs that showed high accuracy in the "Yes vs. No" classification were associated with activation of an area ranging from the parietal to the occipital lobes and were more broadly localized in cortical regions than was seen for the "Yes + No" vs. "I don't know" classification. Our results suggest that small and large delays in real-time cursor control differ not only in the magnitude of the delay but should be handled as distinct information in different ways and might involve differential processing in the brain. [ABSTRACT FROM AUTHOR]

In this study, seven-channel electromyography signal-based two-dimensional wrist joint movement estimation with and without handgrip motions was carried out. Electromyography signals were analyzed using the synergy-based linear regression model and musculoskeletal model; they were subsequently compared with respect to single and combined wrist joint movements and handgrip. Using each one of wrist motion and grip trial as a training set, the synergy-based linear regression model exhibited a statistically significant performance with 0.7891 ± 0.0844 Pearson correlation coefficient (r) value in two-dimensional wrist motion estimation compared with 0.7608 ± 0.1037 r value of the musculoskeletal model. Estimates on the grip force produced 0.8463 ± 0.0503 r value with 0.2559 ± 0.1397 normalized root-mean-square error of the wrist motion range. This continuous wrist and handgrip estimation can be considered when electromyography-based multi-dimensional input signals in the prosthesis, virtual interface, and rehabilitation are needed. [ABSTRACT FROM AUTHOR]

Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology. [ABSTRACT FROM AUTHOR]

EEG-controlled gaming applications range widely from strictly medical to completely nonmedical applications. Games can provide not only entertainment but also strong motivation for practicing, thereby achieving better control with rehabilitation system. In this paper we present real-time control of video game with eye movements for asynchronous and noninvasive communication system using two temporal EEG sensors. We used wavelets to detect the instance of eye movement and time-series characteristics to distinguish between six classes of eye movement. A control interface was developed to test the proposed algorithm in real-time experiments with opened and closed eyes. Using visual feedback, a mean classification accuracy of 77.3% was obtained for control with six commands. And a mean classification accuracy of 80.2% was obtained using auditory feedback for control with five commands. The algorithm was then applied for controlling direction and speed of character movement in two-dimensional video game. Results showed that the proposed algorithm had an efficient response speed and timing with a bit rate of 30 bits/min, demonstrating its efficacy and robustness in real-time control. [ABSTRACT FROM AUTHOR]

Here, we focused on a load-on task to clarify the judgment mechanism for weight perception. We measured hand trajectory and surface electromyography to estimate time-varying stiffness and torque patterns in joints. Our results showed that the goal of learning motor skills under load-on tasks is to maintain the hand position, and that humans produce force with cocontraction and increasing stiffness levels according to the weight of an object. Finally, we examined whether stiffness is an effective parameter as an efferent signal for weight perception and found that stiffness plays an important role in weight perception. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a system for implementing direct, two-handed, multifinger manipulations in a virtual world. To implement an easier-to-use means of direct, two-handed, multifinger manipulations, it is necessary to give a sense of touch to the hands to make them feel like they are grasping an object and to make it easier for the user to understand the positional relationship between the user's hands and the virtual object. Therefore, the authors constructed a system that provides force feedback to eight fingertips and that can display virtual hands making the same movements as the user's actual hands in real time. The system uses the SPIDAR-8 device for force feedback. SPIDAR-8 is a device that can measure the positions of eight points and provide force feedback by using strings. Force feedback can be provided to a fingertip by attaching a cap-like apparatus to the ends of the strings and fitting this apparatus onto the fingertip. The virtual hands are displayed according to the eight fingertip positions calculated by SPIDAR-8. The hand shapes are estimated from the positions of the eight points, and these hands are displayed together with the virtual object via computer graphics. Virtual hands making the same movements as the user's actual hands are displayed in this way. With this kind of system, the user can integrate the haptic and visual information that is presented to efficiently perform direct, two-handed, multifinger manipulations. © 2004 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 87(9): 65–73, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20117 [ABSTRACT FROM AUTHOR]

In this study the authors proposed a method for reinforced learning using multistep state estimation based on internal model of control object. It offers a discussion of learning results obtained by application of this model to a vehicle running on a road including various curvatures, for evaluating whether the vehicle would wander off the road. The vehicle's dynamic model acquired by learning was used for estimating rewards and determining the control outputs, based on the estimated states at multiple time intervals. Results of simulation confirmed that safe driving was possible even with road shapes containing nonlearned curvatures and speeds. Since in this control method external information varies depending on the employed control patterns and environmental conditions, it is possible to consider a model with changing view in order to obtain necessary information for driving control. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(10): 85–95, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10123 [ABSTRACT FROM AUTHOR]

We have been working toward the construction of a forward model arm employing a neural network model with electromyographic signals as the control input. We succeeded in estimating 1) the joint torque during isometric contraction in a plane from the electromyographic signals. 2) the path of motion from the degree of joint acceleration as well as 3) the path of motion from joint torque and 4) the position in three-dimensional space. In this paper, we present a new human interface employing a model of an arm, robot control of an artificial hand, and the learning of motion capability. In addition, based on the fact that we can estimate the position of the arm in three-dimensional space, we discuss as an example of the human interface the results of generating motion from electromyographic signals in a hypothetical arm using a neural network model. [ABSTRACT FROM AUTHOR]