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Hybrid Trajectory Decoding from ECoG Signals for Asynchronous BCIs

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Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9886))

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Abstract

Brain-Computer Interfaces (BCIs) are systems which convert brain neural activity into commands for external devices. BCI users generally alternate between No Control (NC) and Intentional Control (IC) periods. Numerous motor-related BCI decoders focus on the prediction of continuously-valued limb trajectories from neural signals. Although NC/IC discrimination is crucial for clinical BCIs, continuous decoders rarely support NC periods. Integration of NC support in continuous decoders is investigated in the present article. Two discrete/continuous hybrid decoders are compared for the task of asynchronous wrist position decoding from ElectroCorticoGraphic (ECoG) signals in monkeys. One static and one dynamic decoder, namely a Switching Linear (SL) decoder and a Switching Kalman Filter (SKF), are evaluated on high dimensional time-frequency-space ECoG signal representations. The SL decoder was found to outperform the SKF for both NC/IC class detection and trajectory modeling.

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Acknowledgments

This work was supported in part by grants from the French National Research Agency (ANR-Carnot Institute), Fondation Motrice, Fondation Nanosciences, Fondation de l’Avenir, and Fondation Philanthropique Edmond J. Safra. The authors are grateful to all members of the CEA-LETI-CLINATEC, and especially to A. Eliseyev, M. Janvier, G. Charvet, C. Mestais and Prof. A.-L. Benabid.

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Authors and Affiliations

  1. CEA/LETI/CLINATEC, 17 Rue des Martyrs, 38054, Grenoble Cedex 9, France

    Marie-Caroline Schaeffer & Tetiana Aksenova

Authors
  1. Marie-Caroline Schaeffer
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  2. Tetiana Aksenova
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Correspondence to Tetiana Aksenova .

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Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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Schaeffer, MC., Aksenova, T. (2016). Hybrid Trajectory Decoding from ECoG Signals for Asynchronous BCIs. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_34

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

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