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Moving Brain-Controlled Devices Outside the Lab: Principles and Applications

  • Chapter
Recent Progress in Brain and Cognitive Engineering

Part of the book series: Trends in Augmentation of Human Performance ((TAHP,volume 5))

Abstract

This chapter provides an overview of the functionality and the underlying principles of the brain-computer interfaces (BCI) developed by the Chair in Non-Invasive Brain-Machine Interface (CNBI) of the Swiss Federal Institute of Technology (EPFL), as well as exemplary applications where those have been successfully evaluated. Our laboratory mainly develops non-invasive BCI systems based on electroencephalographic (EEG) signals and, thus, devoid of medical hazards, real-time, portable, relatively low-cost and minimally obtrusive. Our research is pushing forward asynchronous paradigms offering a spontaneous, user-driven and largely ecological interaction. Furthermore, we stand on the machine-learning way to BCI with emphasis on personalization, configurability and adaptability, coupled with mutual learning training protocols, so that elaborate signal processing and pattern recognition methods are optimally combined with the user’s learnable modulation of brain signals towards high and robust performances and universal usability. Additionally, cognitive mental state monitoring is employed to shape or refine the interaction. Shared-control approaches allow smart, context-aware robotics to complement the BCI channel for more fine-grained control and reduction of the user’s mental workload. Last but not least, hybrid BCI designs exploit additional physiological signals to augment the BCI modality and enrich the control paradigm, thus also exploiting potential residual capabilities of disabled end-users.

The applicability and effectiveness of the aforementioned principles is hereby demonstrated in four exemplary applications evaluated with both able-bodied and motor-disabled end-users. These applications include a hybrid, motor imagery (MI)-based speller, a telepresence robot equipped with shared-control, cognitive mental state monitoring paradigms able to recognize and correct errors, and, finally, a car driving application where a passive BCI enabled on a smart car assists towards increased safety and improved driving experience. Remarkably, our results show that the performance of end-users with disabilities was similar to that of a group of healthy users, who were more familiar with the experiment and the environment. This demonstrates that end-users are able to successfully use BCI technology.

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Notes

  1. 1.

    This Section follows closely a prior published paper by the authors [66].

  2. 2.

    This Section follows closely a prior published paper by the authors [45].

  3. 3.

    This Section follows closely a prior published paper by the authors [34].

  4. 4.

    This Section follows closely prior published papers by the authors [27, 37].

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Acknowledgements

The authors acknowledge the use of text from their prior publications [27, 34, 37, 45, 66] in Sect. 6.3.

This work was supported by the European ICT programme projects TOBI: Tools for Brain-Computer Interaction (FP7-224631) and Opportunity: Activity and Context Recognition with Opportunistic Sensor Configuration (ICT-225938), the Swiss National Science Foundation NCCR Robotics, Nissan Motor Co. Ltd., the Hasler Foundation, and the dissemination by the European ICT coordination and support action BNCI Horizon 2020 (FP7-ICT-609593). The writing up of this work was also supported by the Swiss canton of Valais-Wallis and by the City of Sion. This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

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    Authors and Affiliations

    1. Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Station 19, CH-1015, Lausanne, Switzerland

      Robert Leeb

    2. Defitech Chair in Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Station 11, CH-1015, Lausanne, Switzerland

      Ricardo Chavarriaga, Serafeim Perdikis, Iñaki Iturrate & José d. R. Millán

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    1. Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea (Republic of)

      Seong-Whan Lee

    2. Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Tübingen, Germany

      Heinrich H. Bülthoff

    3. Machine Learning Group, Berlin Institute of Technology, Berlin, Germany

      Klaus-Robert Müller

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    Leeb, R., Chavarriaga, R., Perdikis, S., Iturrate, I., Millán, J.d.R. (2015). Moving Brain-Controlled Devices Outside the Lab: Principles and Applications. In: Lee, SW., Bülthoff, H., Müller, KR. (eds) Recent Progress in Brain and Cognitive Engineering. Trends in Augmentation of Human Performance, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7239-6_6

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