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Symbiotic Brain-Machine interaction: Beyond control and monitoring

Ricardo Chavarriaga1*
  • 1 École Polytechnique Fédérale de Lausanne, Institute of Bioengineering, Switzerland

Brain-machine interfaces (BMI) extract information from neural activity to be used for neuroprosthetics -e.g., for restoring or substitute lost motor capabilities- (Millán et al., 2010; Min et al., 2017); as well as consumer applications -e.g., gaming, wellness or neuroergonomics- (Lotte et al., 2013; Kerous et al., 2018). BMIs are usually designed to provide commands for direct control of external devices; Alternatively, neural activity can be decoded to infer user states such as cognitive workload, attentional levels or fatigue. In the latter case, these interfaces are mainly used as an assessment tool and the extracted information is seldom used in closed loop to improve human-machine interaction in an online manner. We have proposed an alternative paradigm where BMIs are used to foster symbiotic human-machine interaction. Instead of providing direct control commands, the user's cognitive states — inferred from neural signals— are used online to adapt the machine to the user’s current preferences and capabilities. In this case, intelligent machines can be aware of the information from their environment (i.e., contextual information) but also about their user (i.e. cognitive context provided by the BMI) (Saeedi et al., 2015). Development of Symbiotic BMIs entails multiple challenges. They should be able to reliably detect cognitive states in real situations (Makeig et al., 2009; Zhang et al., 2015). In turn, the human-machine loop should be endowed with unsupervised adaptive mechanisms that allow online modulation of the assistance provided by machines (Chavarriaga et al., 2014; Iturrate et al., 2015; Zander et al., 2016). Advances in sensor capabilities, virtual reality, and machine learning offer a great opportunity to achieve this vision, augment our knowledge of the nervous system and develop more robust human-machine interfaces. Examples from the automotive industry (Chavarriaga et al., 2018), assistive technologies (Saeedi et al., 2017) and human-robot interaction (Iturrate et al., 2015) will be presented to illustrate how state-of-the-interfaces allow for effectively keeping the human-in-the-loop promoting symbiotic interactions between machines and their users.

Acknowledgements

This work was supported in part by Nissan Motor Co. Ltd., under the “Research on Brain Machine Interface for Drivers” project and in part by the SNSF-funded NCCR Robotics.

References

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Keywords: Brain Computer Interface, error potentials (ErrP), neuroergonomics, cognitive state assessment, human machine symbiosis

Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018.

Presentation Type: Oral Presentation

Topic: Neuroergonomics

Citation: Chavarriaga R (2019). Symbiotic Brain-Machine interaction: Beyond control and monitoring. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00010

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Received: 21 Aug 2018; Published Online: 27 Sep 2019.

* Correspondence: Dr. Ricardo Chavarriaga, École Polytechnique Fédérale de Lausanne, Institute of Bioengineering, Lausanne, 1015, Switzerland, ricardo.chavarriaga@alumni.epfl.ch