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Yoshimura N.,Tokyo Institute of Technology | Satsuma A.,Tokyo Institute of Technology | Dasalla C.S.,National Institute of Neuroscience | Hanakawa T.,National Institute of Neuroscience | And 2 more authors.
2011 International Conference on Virtual Rehabilitation, ICVR 2011 | Year: 2011

With the purpose of providing assistive technology for the communication impaired, we propose a new approach for speech prostheses using vowel speech imagery. Using a hierarchical Bayesian method, electroencephalography (EEG) cortical currents were estimated using EEG signals recorded from three healthy subjects during the performance of three tasks, imaginary speech of vowels /a/ and /u/, and a no imagery state as control. The 3-task classification using a sparse logistic regression method with variational approximation (SLR-VAR) revealed that mean classification accuracy of cortical currents was almost two times greater than chance level and significantly higher than that using EEG signals. The results suggest the possibility of using EEG cortical currents to discriminate multiple syllables by improving the spatial discrimination of EEG. © 2011 IEEE.

D'Arrigo S.,Fondazione Istituto Neurologico C. Besta | Bulgheroni S.,Fondazione Istituto Neurologico C. Besta | Imamizu H.,Advanced Telecommunication Research Institute International | Imamizu H.,Osaka University | And 4 more authors.
Cerebellum | Year: 2014

While the cerebellum's role in motor function is well recognized, the nature of its concurrent role in cognitive function remains considerably less clear. The current consensus paper gathers diverse views on a variety of important roles played by the cerebellum across a range of cognitive and emotional functions. This paper considers the cerebellum in relation to neurocognitive development, language function, working memory, executive function, and the development of cerebellar internal control models and reflects upon some of the ways in which better understanding the cerebellum's status as a "supervised learning machine" can enrich our ability to understand human function and adaptation. As all contributors agree that the cerebellum plays a role in cognition, there is also an agreement that this conclusion remains highly inferential. Many conclusions about the role of the cerebellum in cognition originate from applying known information about cerebellar contributions to the coordination and quality of movement. These inferences are based on the uniformity of the cerebellum's compositional infrastructure and its apparent modular organization. There is considerable support for this view, based upon observations of patients with pathology within the cerebellum. © Springer Science+Business Media 2013.

Samek W.,TU Berlin | Kawanabe M.,Advanced Telecommunication Research Institute International | Muller K.-R.,TU Berlin | Muller K.-R.,Korea University
IEEE Reviews in Biomedical Engineering | Year: 2014

Controlling a device with a brain-computer interface requires extraction of relevant and robust features from high-dimensional electroencephalographic recordings. Spatial filtering is a crucial step in this feature extraction process. This paper reviews algorithms for spatial filter computation and introduces a general framework for this task based on divergence maximization. We show that the popular common spatial patterns (CSP) algorithm can be formulated as a divergence maximization problem and computed within our framework. Our approach easily permits enforcing different invariances and utilizing information from other subjects; thus, it unifies many of the recently proposed CSP variants in a principled manner. Furthermore, it allows to design novel spatial filtering algorithms by incorporating regularization schemes into the optimization process or applying other divergences. We evaluate the proposed approach using three regularization schemes, investigate the advantages of beta divergence, and show that subject-independent feature spaces can be extracted by jointly optimizing the divergence problems of multiple users. We discuss the relations to several CSP variants and investigate the advantages and limitations of our approach with simulations. Finally, we provide experimental results on a dataset containing recordings from 80 subjects and interpret the obtained patterns from a neurophysiological perspective. © 2008-2011 IEEE.

Shibata K.,Advanced Telecommunication Research Institute International
Clinical Neurology | Year: 2012

Neurofeedback is defined as a method to read out information from the brain and feed the information back to the brain. This technology has developed in the past ten years and attracted considerable attention as potential treatments for rehabilitation and psychiatric disease. We recently invented the decoded neurofeedback (DecNef) method, a new neurofeedback technique using functional magnetic resonance imaging. With DecNef, subjects were trained to regulate their brain activation pattern in a specific area and lead the pattern to a target state. We found that the DecNef training for several days leads to perceptual improvement that corresponds to the induced target state. DecNef enables us to test cause-and-effect relationships between neural activation in a target brain area and changes in perception, cognition, and behavior. In this sense, this method can be a powerful tool in cognitive and systems neuroscience. In addition, the concept of DecNef, leading a neural activation pattern to a specific state,can be applied for a variety of fields including engineering and medical treatment.

Nakanishi J.,Osaka University | Nakanishi J.,Advanced Telecommunication Research Institute International | Sakatani Y.,Osaka University | Okubo M.,Osaka University | And 5 more authors.
Proceedings - 2016 IEEE 10th International Conference on Semantic Computing, ICSC 2016 | Year: 2016

This paper explores the notion of self-agency in developing agent-based systems that support human-to-human communication. We first point out that a challenge in developing such agent-based systems is to successfully transfer conversational experiences that agents gain to their users. We then propose that the sense of self-agency is a key to address this challenge. We also show an experimental system to examine the impact of the sense of self-agency on the successful transfer of conversational experiences from agents to their users. © 2016 IEEE.

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