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Consciousness Detection in Complete Locked-In State Patients Using Electroencephalogram Coherency and Artificial Neural Networks

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Sensor Networks and Signal Processing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 176))

Abstract

In this study, a method to uncover levels of consciousness using electroencephalogram (EEG) coherency and artificial neural network is presented. The subjects of interest are complete locked-in syndrome (CLIS) patients. These patients are characterized by complete paralysis and sufficiently intact cognition. Consequently, they are aware of themselves and their surroundings, but are unable to produce speech. A great challenge in the study of consciousness in patients with CLIS is that there are no certainty regarding their level of awareness at all time. In this paper, a method using EEG coherence matrices as input to a convolutional autoencoder to determine a patient’s level of consciousness is presented. The ultimate goal of the research is to build a brain–computer interface-based communication device to allow interactions with CLIS patients.

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Acknowledgements

Data were kindly provided by Prof. Dr. Dr. hc. mult. Niels Birbaumer and Dr. Ujwal Chaudhary from the Institute for Medical Psychology and Behavioural Neurobiology, University of Tübingen.

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Authors and Affiliations

  1. Department of Computer Engineering, Leipzig University, Augustusplatz 10, 04109, Leipzig, Germany

    V. S. Adama & Martin Bogdan

Authors
  1. V. S. Adama
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  2. Martin Bogdan
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Correspondence to V. S. Adama .

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Editors and Affiliations

  1. Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  2. Informatics and Computer Techniques, Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  3. Department of Electrical Engineering, National Dong Hwa University, Hualien, Taiwan

    Han-Chieh Chao

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Adama, V.S., Bogdan, M. (2021). Consciousness Detection in Complete Locked-In State Patients Using Electroencephalogram Coherency and Artificial Neural Networks. In: Peng, SL., Favorskaya, M., Chao, HC. (eds) Sensor Networks and Signal Processing. Smart Innovation, Systems and Technologies, vol 176. Springer, Singapore. https://doi.org/10.1007/978-981-15-4917-5_29

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  • DOI: https://doi.org/10.1007/978-981-15-4917-5_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4916-8

  • Online ISBN: 978-981-15-4917-5

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