Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

Article English OPEN
Abu-Alqumsan, M.; Ebert, F.; Peer, A.;
(2017)
  • Publisher: IOP Publishing

Objective: This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry applicat... View more
  • References (56)
    56 references, page 1 of 6

    [1] Jonathan R Wolpaw, Niels Birbaumer, Dennis J McFarland, Gert Pfurtscheller, and Theresa M Vaughan. Brain-computer interfaces for communication and control. Clinical neurophysiology, 113(6):767{791, jun 2002.

    [2] Mike Chung, Willy Cheung, Reinhold Scherer, and Rajesh P. N. Rao. A hierarchical architecture for adaptive brain-computer interfacing. In Proceedings of the Twenty-Second international joint conference on Arti cial Intelligence, pages 1647{1652, 2011.

    [3] Matthew Bryan, Joshua Green, Mike Chung, Lillian Chang, Reinhold Scherer, Joshua Smith, and Rajesh P. N. Rao. An adaptive brain-computer interface for humanoid robot control. In 11th IEEE-RAS International Conference on Humanoid Robots, pages 199{204, Bled, Slovenia, 2011.

    [4] Carlos Escolano, Javier Mauricio Antelis, and Javier Minguez. A telepresence mobile robot controlled with a noninvasive brain-computer interface. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics, 42(3):793{804, jun 2012.

    [5] Ori Cohen, Sebastien Druon, Sebastien Lengagne, Avi Mendelsohn, Rafael Malach, Abderrahmane Kheddar, and Doron Friedman. fMRI based robotic embodiment: a pilot study. In IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pages 314{319, 2012.

    [6] M. Alimardani, S. Nishio, and H. Ishiguro. BCIteleoperated androids; a study of embodiment and its e ect on motor imagery learning. In 2015 IEEE 19th International Conference on Intelligent Engineering Systems (INES), pages 347{352, 2015.

    [7] Emmanuele Tidoni, Pierre Gergondet, Gabriele Fusco, Abderrahmane Kheddar, and Salvatore Aglioti. The role of audio-visual feedback in a thought-based control of a humanoid robot: a BCI study in healthy and spinal cord injured people. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 4320(c):1{1, 2016.

    [8] Mel Slater and Maria Sanchez-Vives. Enhancing Our Lives with Immersive Virtual Reality. Frontiers in Robotics and AI, 3(December):74, 2016.

    [9] Konstantina Kilteni, Raphaela Groten, and Mel Slater. The Sense of Embodiment in Virtual Reality. Presence: Teleoperators and Virtual Environments, 21(4):373{387, 2012.

    [10] Saul Greenberg, John J. Darragh, David Maulsby, and Ian H. Witten. Predictive interfaces: What will they think of next? In A. D. N Edwards, editor, Extra-ordinary human-computer interaction: interfaces for users with disabilities, pages 103{140. Cambridge University Press, 1995.

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    FET FP7FET Proactive: FET proactive 4: Human-Computer Confluence
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