Sasaki H.,University of Electro - Communications |
Gutmann M.U.,University of Helsinki |
Shouno H.,University of Electro - Communications |
Hyvarinen A.,University of Helsinki |
Hyvarinen A.,ATR Cognitive Mechanisms Laboratories
Journal of Machine Learning Research | Year: 2014
The statistical dependencies which independent component analysis (ICA) cannot remove often provide rich information beyond the ICA components. It would be very useful to estimate the dependency structure from data. However, most models have concentrated on higher-order correlations such as energy correlations, neglecting linear correlations. Linear correlations might be a strong and informative form of a dependency for some real data sets, but they are usually completely removed by ICA and related methods, and not analyzed at all. In this paper, we propose a probabilistic model of non-Gaussian components which are allowed to have both linear and energy correlations. The dependency structure of the components is explicitly parametrized by a parameter matrix, which defines an undirected graphical model over the latent components. Furthermore, the estimation of the parameter matrix is shown to be particularly simple because using score matching, the objective function is a quadratic form. Using artificial data, we demonstrate that the proposed method is able to estimate non-Gaussian components and their dependency structures, as it is designed to do. When applied to natural images and outputs of simulated complex cells in the primary visual cortex, novel dependencies between the estimated features are discovered.
Manto M.,Unite DEtude du Mouvement UEM |
Bower J.M.,University of Texas Health Science Center at San Antonio |
Conforto A.B.,University of Sao Paulo |
Conforto A.B.,Instituto Israelita Of Ensino E Pesquisa Albert Einstein |
And 22 more authors.
Cerebellum | Year: 2012
Considerable progress has been made in developing models of cerebellar function in sensorimotor control, as well as in identifying key problems that are the focus of current investigation. In this consensus paper, we discuss the literature on the role of the cerebellar circuitry in motor control, bringing together a range of different viewpoints. The following topics are covered: oculomotor control, classical conditioning (evidence in animals and in humans), cerebellar control of motor speech, control of grip forces, control of voluntary limb movements, timing, sensorimotor synchronization, control of corticomotor excitability, control of movement-related sensory data acquisition, cerebro-cerebellar interaction in visuokinesthetic perception of hand movement, functional neuroimaging studies, and magnetoencephalographic mapping of cortico-cerebellar dynamics. While the field has yet to reach a consensus on the precise role played by the cerebellum in movement control, the literature has witnessed the emergence of broad proposals that address cerebellar function at multiple levels of analysis. This paper highlights the diversity of current opinion, providing a framework for debate and discussion on the role of this quintessential vertebrate structure. ©Springer Science+Business Media, LLC 2011.
Yamagishi N.,ATR Cognitive Mechanisms Laboratories |
Yamagishi N.,Japan Science and Technology Agency |
Yamagishi N.,Japan National Institute of Information and Communications Technology |
Anderson S.J.,Aston University
PLoS ONE | Year: 2013
High-level cognitive factors, including self-awareness, are believed to play an important role in human visual perception. The principal aim of this study was to determine whether oscillatory brain rhythms play a role in the neural processes involved in self-monitoring attentional status. To do so we measured cortical activity using magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) while participants were asked to self-monitor their internal status, only initiating the presentation of a stimulus when they perceived their attentional focus to be maximal. We employed a hierarchical Bayesian method that uses fMRI results as soft-constrained spatial information to solve the MEG inverse problem, allowing us to estimate cortical currents in the order of millimeters and milliseconds. Our results show that, during self-monitoring of internal status, there was a sustained decrease in power within the 7-13 Hz (alpha) range in the rostral cingulate motor area (rCMA) on the human medial wall, beginning approximately 430 msec after the trial start (p < 0.05, FDR corrected). We also show that gamma-band power (41-47 Hz) within this area was positively correlated with task performance from 40-640 msec after the trial start (r = 0.71, p < 0.05). We conclude: (1) the rCMA is involved in processes governing self-monitoring of internal status; and (2) the qualitative differences between alpha and gamma activity are reflective of their different roles in self-monitoring internal states. We suggest that alpha suppression may reflect a strengthening of top-down interareal connections, while a positive correlation between gamma activity and task performance indicates that gamma may play an important role in guiding visuomotor behavior. © 2013 Yamagishi et al.
Suyama T.,ATR Cognitive Mechanisms Laboratories
IEICE Transactions on Communications | Year: 2016
To help elderly and physically disabled people to become self-reliant in daily life such as at home or a health clinic, we have developed a network-type brain machine interface (BMI) system called "network BMI" to control real-world actuators like wheelchairs based on human intention measured by a portable brain measurement system. In this paper, we introduce the technologies for achieving the network BMI system to support activities of daily living. © Copyright 2016 The Institute of Electronics Information and Communication Engineers.
Moore B.,ATR Cognitive Mechanisms Laboratories |
Moore B.,Laval University |
Oztop E.,ATR Cognitive Mechanisms Laboratories |
Oztop E.,NICT Biological ICT Group |
Oztop E.,Ozyegin University
Robotics and Autonomous Systems | Year: 2012
A major goal of robotics research is to develop techniques that allow non-experts to teach robots dexterous skills. In this paper, we report our progress on the development of a framework which exploits human sensorimotor learning capability to address this aim. The idea is to place the human operator in the robot control loop where he/she can intuitively control the robot, and by practice, learn to perform the target task with the robot. Subsequently, by analyzing the robot control obtained by the human, it is possible to design a controller that allows the robot to autonomously perform the task. First, we introduce this framework with the ball-swapping task where a robot hand has to swap the position of the balls without dropping them, and present new analyses investigating the intrinsic dimension of the ball-swapping skill obtained through this framework. Then, we present new experiments toward obtaining an autonomous grasp controller on an anthropomorphic robot. In the experiments, the operator directly controls the (simulated) robot using visual feedback to achieve robust grasping with the robot. The data collected is then analyzed for inferring the grasping strategy discovered by the human operator. Finally, a method to generalize grasping actions using the collected data is presented, which allows the robot to autonomously generate grasping actions for different orientations of the target object. © 2011 Elsevier B.V. All rights reserved.
Gurbuz S.,Universal Communication Research Institute |
Oztop E.,ATR Cognitive Mechanisms Laboratories |
Oztop E.,NICT Advanced ICT Research Institute |
Inoue N.,Universal Communication Research Institute
Pattern Recognition | Year: 2012
In this paper we present a stereovision based model free 3D head pose (orientation and position) estimation system suitable for humanmachine interface applications. The system works by obtaining a 'face plane' from the 3D reconstructed face data, which is then used for head pose estimation. The key novelty in this work is the utilization of the face plane together with the eye locations on the reconstructed face data to obtain a robust head pose estimate. This approach leads to a model and initialization free head pose estimation system; therefore it is suitable for natural humanmachine interfaces. In order to quantitatively asses the accuracy of the system for such applications, several evaluation experiments were conducted using a commercial motion capture system. The evaluation results indicate that this system can be used in humancomputer and humanrobot applications. © 2011 Elsevier Ltd. All rights reserved.
Ugur E.,Biological ICT Group |
Ugur E.,Middle East Technical University |
Ugur E.,ATR Cognitive Mechanisms Laboratories |
Oztop E.,Biological ICT Group |
And 2 more authors.
Robotics and Autonomous Systems | Year: 2011
In this paper, we show that through self-interaction and self-observation, an anthropomorphic robot equipped with a range camera can learn object affordances and use this knowledge for planning. In the first step of learning, the robot discovers commonalities in its action-effect experiences by discovering effect categories. Once the effect categories are discovered, in the second step, affordance predictors for each behavior are obtained by learning the mapping from the object features to the effect categories. After learning, the robot can make plans to achieve desired goals, emulate end states of demonstrated actions, monitor the plan execution and take corrective actions using the perceptual structures employed or discovered during learning. We argue that the learning system proposed shares crucial elements with the development of infants of 710 months age, who explore the environment and learn the dynamics of the objects through goal-free exploration. In addition, we discuss goal emulation and planning in relation to older infants with no symbolic inference capability and non-linguistic animals which utilize object affordances to make action plans. © 2011 Elsevier B.V. All rights reserved.
Okadome Y.,Osaka University |
Nakamura Y.,Osaka University |
Nakamura Y.,ATR Cognitive Mechanisms Laboratories |
Shikauchi Y.,Kyoto University |
And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013
Gaussian process regression (GPR) has the ability to deal with non-linear regression readily, although the calculation cost increases with the sample size. In this paper, we propose a fast approximation method for GPR using both locality-sensitive hashing and product of experts models. To investigate the performance of our method, we apply it to regression problems, i.e., artificial data and actual hand motion data. Results indicate that our method can perform accurate calculation and fast approximation of GPR even if the dataset is non-uniformly distributed. © 2013 Springer-Verlag Berlin Heidelberg.
Morales Y.,Communication Intelligence |
Abdur-Rahim J.A.,ATR Cognitive Mechanisms Laboratories |
Even J.,Communication Intelligence |
Watanabe A.,Communication Intelligence |
And 4 more authors.
IEEE International Conference on Intelligent Robots and Systems | Year: 2014
This work proposes a model for human habituation while riding a robotic wheelchair. We present and describe the concept of human navigational habituation which we define as the human habituation to repetitively riding a robotic wheelchair. The approach models habituation in terms of preferred linear velocity based on the experience of riding a wheelchair. We argue that preferred velocity changes as the human gets used to riding on the wheelchair. Inexperienced users initially prefer to ride at a slow moderate pace, however the longer they ride they prefer to speed up to a certain comfort level and find initial slower velocities to be tediously 'too slow' for their experience level. The proposed habituation model provides passenger preferred velocity based on experience. Human biological measurements, galvanic skin conductance, and participant feedback demonstrate the preference for habituation velocity control over fixed velocity control. To our knowledge habituation modeling is new in the field of autonomous navigation and robotics. © 2014 IEEE.
PubMed | Japan National Institute of Advanced Industrial Science and Technology, ATR Cognitive Mechanisms Laboratories and Kyoto University
Type: | Journal: NeuroImage | Year: 2015
Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments.