Time filter

Source Type

Ikegami S.,Research Institute of National Rehabilitation Center for Persons with Disabilities | Ikegami S.,Chiba University | Takano K.,Research Institute of National Rehabilitation Center for Persons with Disabilities | Saeki N.,Chiba University | Kansaku K.,Research Institute of National Rehabilitation Center for Persons with Disabilities
Clinical Neurophysiology | Year: 2011

Objective: This study evaluates the efficacy of a P300-based brain-computer interface (BCI) with green/blue flicker matrices for individuals with cervical spinal cord injury (SCI). Methods: Ten individuals with cervical SCI (age 26-53, all male) and 10 age- and sex-matched able-bodied controls (age 27-52, all male) with no prior BCI experience were asked to input hiragana (Japanese alphabet) characters using the P300 BCI with two distinct types of visual stimuli, white/gray and green/blue, in an 8×10 flicker matrix. Both online and offline performance were evaluated. Results: The mean online accuracy of the SCI subjects was 88.0% for the white/gray and 90.7% for the green/blue flicker matrices. The accuracy of the control subjects was 77.3% and 86.0% for the white/gray and green/blue, respectively. There was a significant difference in online accuracy between the two types of flicker matrix. SCI subjects performed with greater accuracy than controls, but the main effect was not significant. Conclusions: Individuals with cervical SCI successfully controlled the P300 BCI, and the green/blue flicker matrices were associated with significantly higher accuracy than the white/gray matrices. Significance: The P300 BCI with the green/blue flicker matrices is effective for use not only in able-bodied subjects, but also in individuals with cervical SCI. © 2010 International Federation of Clinical Neurophysiology.


Komoto K.,Research Institute of National Rehabilitation Center for Persons with Disabilities | Suzurikawa J.,Research Institute of National Rehabilitation Center for Persons with Disabilities
2012 IEEE/SICE International Symposium on System Integration, SII 2012 | Year: 2012

To comprehensively evaluate the usability and maneuverability of power wheelchairs (PWCs), it is important to systematically collect multimodal data related to wheelchair use from a real environment. In this study, we developed a wheelchair everyday life log system with smartphone-based electronic recording equipment (WELL-SphERE). The combination of a smartphone and a versatile A/D converter allows the measurement, transfer, and storage of various indicators, including the three-axis accelerations and angular velocities of a wheelchair body, GPS position, and joystick inputs. By employing a triaxial accelerometer mounted on the tip of a joystick, the system can directly quantify the joystick angular displacement without any modification of the existing driving system. We installed this system on two different types of PWCs and characterized the data logged during the test-driving. The comparison of the joystick inputs logged by the WELL-SphERE system with those electronically logged from the driving unit identified measurement errors caused by inertial movements of the wheelchair bodies. These errors, however, were ignorable if we consider the dead zone of the joystick displacements. A superimposed plot of the z-axis acceleration on a map demonstrates the effectiveness of WELL-SphERE in grasping the relationship between the terrain conditions and wheelchair behavior. © 2012 IEEE.


Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
PLoS ONE | Year: 2015

One of the core features of autism spectrum disorder (ASD) is impaired reciprocal social interaction, especially in processing emotional information. Social robots are used to encourage children with ASD to take the initiative and to interact with the robotic tools to stimulate emotional responses. However, the existing evidence is limited by poor trial designs. The purpose of this study was to provide computational evidence in support of robot-Assisted therapy for children with ASD. We thus propose an emotional model of ASD that adapts a Bayesian model of the uncanny valley effect, which holds that a human-looking robot can provoke repulsion and sensations of eeriness. Based on the unique emotional responses of children with ASD to the robots, we postulate that ASD induces a unique emotional response curve, more like a cliff than a valley. Thus, we performed numerical simulations of robot-Assisted therapy to evaluate its effects. The results showed that, although a stimulus fell into the uncanny valley in the typical condition, it was effective at avoiding the uncanny cliff in the ASD condition. Consequently, individuals with ASD may find it more comfortable, and may modify their emotional response, if the robots look like deformed humans, even if they appear "creepy" to typical individuals. Therefore, we suggest that our model explains the effects of robot-Assisted therapy in children with ASD and that humanlooking robots may have potential advantages for improving social interactions in ASD. Copyright: © 2015 Yuki Ueyama.


Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
International Workshop on Advanced Motion Control, AMC | Year: 2014

We investigated the role of feedback gain in optimal feedback control (OFC) theory using a neuromotor system. Neural studies have shown that directional tuning, known as the 'preferred direction' (PD), is a basic functional property of cell activity in the primary motor cortex (M1). However, it is not clear which directions the M1 codes for, because neural activities can correlate with several directional parameters, such as joint torque and end-point motion. Thus, to examine the computational mechanism in the M1, we modeled the isometric motor task of a musculoskeletal system required to generate the desired joint torque. Then, we computed the optimal feedback gain according to OFC. The feedback gain indicated directional tunings of the joint torque and end-point motion in Cartesian space that were similar to the M1 neuron PDs observed in previous studies. Thus, we suggest that the M1 acts as a feedback gain in OFC. © 2014 IEEE.


Shinya M.,University of Tokyo | Kawashima N.,Research Institute of National Rehabilitation Center for Persons with Disabilities | Nakazawa K.,University of Tokyo
Frontiers in Human Neuroscience | Year: 2016

The central nervous system takes advantage of prior knowledge about potential upcoming perturbations for modulating postural reflexes. There are two distinct aspects of prior knowledge: spatial and temporal. This study investigated how each of spatial and temporal prior knowledge contributes to the shortening of muscle response latency. Eleven participants walked on a split-belt treadmill and perturbed by sudden acceleration or deceleration of the right belt at right foot contact. Spatial prior knowledge was given by instruction of possible direction (e.g., only acceleration) of upcoming perturbation at the beginning of an experimental session. Temporal prior knowledge was given to subjects by warning tones at foot contact during three consecutive strides before the perturbation. In response to acceleration perturbation, reflexive muscle activity was observed in soleus (SOL) and gastrocnemius (GAS) muscles. Onset latency of the GAS response was shorter (72 ms vs. 58 ms) when subjects knew the timing of the upcoming perturbation, whereas the latency was independent of directional prior knowledge. SOL onset latency (44 ms) was not influenced by directional nor temporal prior knowledge. Although spinal neural circuit that mediates short-latency reflex was not influenced by the prior knowledge, excitability in supra-spinal neural circuit that mediates medium- and long-latency reflex might be enhanced by knowing the timing of the upcoming perturbation. © 2016 Shinya, Kawashima and Nakazawa.


Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

We evaluated the efficiency of robotic therapy for stroke survivors by using a computational approach in motor theory with a stroke rehabilitation model. In computational neuroscience, hand movement can be represented by population coding of neuronal preferred directions (PDs) in the motor cortex. We modeled the recovery processes of arm movement in conventional and robotic therapies as reoptimization of PDs in different learning rules, and compared the efficiencies after stroke. Conventional therapy did not induce complete recovery of stroke lesions, and the neuronal state depended on the training direction. However, robotic therapy reoptimized the PDs uniformly regardless of the training direction. These observations suggest that robotic therapy may be effective for recovery and not have a negative effect on motor performance depending the training direction. Furthermore, this study provides computational evidence to promote robotic therapy for stroke rehabilitation. © 2014 Springer International Publishing.


Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

We propose a computational model for anti-Bayesian sensory integration of human behavioral actions and perception in the size-weight illusion (SWI). The SWI refers to the fact that people judge the smaller of two equally weighted objects to heavier when lifted. Many aspects of human perceptual and motor behavior can be modeled with Bayesian statistics. However, the SWI cannot be explained on the basis of Bayesian integration, and the nervous system is thought to use two entirely different mechanisms to integrate prior expectations with current sensory information about object weight. Our proposed model is defined as a state estimator, combining a Kalman filter and a H∞ filter. As a result, the model not only predicted the anti-Bayesian estimation of the weight but also the Bayesian estimation of the motor behavior. Therefore, we hypothesize that the SWI is realized by a H∞ filter and a Kalman filter. © Springer International Publishing Switzerland 2014.


Ueyama Y.,Research Institute of National Rehabilitation Center for Persons with Disabilities
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2015

Several studies have aimed to provide computational evidence of poststroke interventions because how to optimize motor recovery has been unclear. Although muscle synergies may be the basic control modules on which the central nervous system relies to generate motion, previous computational evidence ignored muscle activity. This study proposes a model of motor impairment after stroke for predicting muscle activity patterns. This model can reproduce a peculiar muscle activation pattern observed in stroke patients. Moreover, we carried out a simulation of the motor recovery process by minimizing the output torque error. As a result, the muscle activation patterns could not be modified to the intact condition, because the recovery process might fall into a local minimum. Thus, we suggest that our model could reproduce muscle activities after stroke, and that muscle synergy cannot be recovered by ‘conventional’ processes of the poststroke rehabilitation. © Springer International Publishing Switzerland 2015.


Toyama S.,Research Institute of National Rehabilitation Center for Persons with Disabilities | Takano K.,Research Institute of National Rehabilitation Center for Persons with Disabilities | Kansaku K.,Research Institute of National Rehabilitation Center for Persons with Disabilities
Frontiers in Neurology | Year: 2012

A non-invasive brain-machine interface (BMI) or brain-computer interface is a technology for helping individuals with disabilities and utilizes neurophysiological signals from the brain to control external machines or computers without requiring surgery. However, when applying electroencephalography (EEG) methodology, users must place EEG electrodes on the scalp each time, and the development of easy-to-use electrodes for clinical use is required. In this study, we developed a conductive non-adhesive solid-gel electrode for practical non-invasive BMIs.We performed basic material testing, including examining the volume resistivity, viscoelasticity, and moisture-retention properties of the solid-gel. Then, we compared the performance of the solid-gel, a conventional paste, and an in-house metalpin- based electrode using impedance measurements and P300-BMI testing. The solid-gel was observed to be conductive (volume resistivity 13.22cm) and soft (complex modulus 105.4 kPa), and it remained wet for a prolonged period (>10 h) in a dry environment. Impedance measurements revealed that the impedance of the solid-gel-based and conventional paste-based electrodes was superior to that of the pin-based electrode.The EEG measurement suggested that the signals obtained with the solid-gel electrode were comparable to those with the conventional paste-based electrode. Moreover, the P300-BMI study suggested that systems using the solid-gel or pin-based electrodes were effective. One of the advantages of the solid-gel is that it does not require cleaning after use, whereas the conventional paste adheres to the hair, which requires washing. Furthermore, the solid-gel electrode was not painful compared with a metal-pin electrode.Taken together, the results suggest that the solid-gel electrode worked well for practical BMIs and could be useful for bedridden patients such as those with amyotrophic lateral sclerosis. © 2012 Toyama, Takano and Kansaku.


Halder S.,University of Würzburg | Halder S.,Research Institute of National Rehabilitation Center for Persons with Disabilities | Kathner I.,University of Würzburg | Kubler A.,University of Würzburg
Clinical Neurophysiology | Year: 2016

Objective: Auditory brain-computer interfaces are an assistive technology that can restore communication for motor impaired end-users. Such non-visual brain-computer interface paradigms are of particular importance for end-users that may lose or have lost gaze control. We attempted to show that motor impaired end-users can learn to control an auditory speller on the basis of event-related potentials. Methods: Five end-users with motor impairments, two of whom with additional visual impairments, participated in five sessions. We applied a newly developed auditory brain-computer interface paradigm with natural sounds and directional cues. Results: Three of five end-users learned to select symbols using this method. Averaged over all five end-users the information transfer rate increased by more than 1800% from the first session (0.17 bits/min) to the last session (3.08 bits/min). The two best end-users achieved information transfer rates of 5.78 bits/min and accuracies of 92%. Conclusions: Our results show that an auditory BCI with a combination of natural sounds and directional cues, can be controlled by end-users with motor impairment. Training improves the performance of end-users to the level of healthy controls. Significance: To our knowledge, this is the first time end-users with motor impairments controlled an auditory brain-computer interface speller with such high accuracy and information transfer rates. Further, our results demonstrate that operating a BCI with event-related potentials benefits from training and specifically end-users may require more than one session to develop their full potential. © 2015 International Federation of Clinical Neurophysiology.

Loading Research Institute of National Rehabilitation Center for Persons with Disabilities collaborators
Loading Research Institute of National Rehabilitation Center for Persons with Disabilities collaborators