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An EEG Adaptive Information System for an Empathic Robot

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Abstract

This article introduces a speech-driven information system for a humanoid robot that is able to adapt its information presentation strategy according to brain patterns of its user. Brain patterns are classified from electroencephalographic (EEG) signals and correspond to situations of low and high mental workload. The robot dynamically selects the information presentation style that best matches the detected patterns. The resulting end-to-end system consisting of recognition and adaptation components is tested in an evaluation study with 20 participants. We achieve a mean recognition rate of 83.5% for discrimination between low and high mental workload. Furthermore, we compare the dynamic adaptation strategy with two static presentation strategies. The evaluation results show that the adaptation of the presentation strategy according to workload improves over the static presentation strategy in both, information correctness and completeness. In addition, the adaptive strategy is favored over the static strategy as user satisfaction improves significantly. This paper presents the first systematic analysis of a real-time EEG-adaptive end-to-end information system for a humanoid robot. The achieved evaluation results indicate its great potential for empathic human-robot interaction.

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References

  1. Heger D, Putze F, Schultz T (2010) An adaptive information system for an empathic robot using EEG data. In: International conference on social robotics. Springer, Berlin, pp 151–160

    Google Scholar 

  2. Breazeal C (2004) Designing sociable robots. MIT Press, Cambridge

    Google Scholar 

  3. Torrey C, Powers A, Marge M, Fussell S, Kiesler S (2006) Effects of adaptive robot dialogue on information exchange and social relations. In: 1st ACM SIGCHI/SIGART conference on human-robot interaction, pp 126–133

    Chapter  Google Scholar 

  4. Liu C, Rani P, Sarkar N (2006) Human-robot interaction using affective cues. In: 15th IEEE international symposium on robot and human interactive communication (ROMAN), pp 285–290

    Chapter  Google Scholar 

  5. Bonarini A, Mainardi L, Matteucci M, Tognetti S, Colombo R (2008) Stress recognition in a robotic rehabilitation task. In: Robotic helpers: user interaction, interfaces and companions in assistive and therapy robotics, a workshop at ACM/IEEE HRI

    Google Scholar 

  6. Wolpaw J, Birbaumer N, McFarland D, Pfurtscheller G, Vaughan T (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113(6):767

    Article  Google Scholar 

  7. Mason S, Bashashati A, Fatourechi M, Navarro K, Birch G (2007) A comprehensive survey of brain interface technology designs. Ann Biomed Eng 35(2):137

    Article  Google Scholar 

  8. Bell C, Shenoy P, Chalodhorn R, Rao R (2008) Control of a humanoid robot by a noninvasive brain–computer interface in humans. J Neural Eng 5:214

    Article  Google Scholar 

  9. Millan J, Renkens F, Mouriño J, Gerstner W (2004) Noninvasive brain-actuated control of a mobile robot by human EEG. IEEE Trans Biomed Eng 51(6):1026

    Article  Google Scholar 

  10. McFarland D, Wolpaw J (2008) Brain-computer interface operation of robotic and prosthetic devices. Computer 41(10):52

    Article  Google Scholar 

  11. McFarland D, Sarnacki W, Wolpaw J (2010) Electroencephalographic (EEG) control of three-dimensional movement. J Neural Eng 7:036007

    Article  Google Scholar 

  12. Berka C, Levendowski D, Cvetinovic M, Petrovic M, Davis G, Lumicao M, Zivkovic V, Popovic M, Olmstead R (2004) Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. Int J Hum-Comput Interact 17(2):151

    Article  Google Scholar 

  13. Wilson G, Russell C (2007) Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding. Hum Factors 49(6):1005

    Article  Google Scholar 

  14. Chen D, Vertegaal R (2004) Using mental load for managing interruptions in physiologically attentive user interfaces. In: CHI’04 extended abstracts on human factors in computing systems, pp 1513–1516

    Google Scholar 

  15. Kohlmorgen J, Dornhege G, Braun M, Blankertz B, Muller K, Curio G, Hagemann K, Bruns A, Schrauf M, Kincses W (2007) In: Toward brain-computer interfacing. MIT Press, Cambridge, pp 409–422

    Google Scholar 

  16. Gevins A, Smith M (2003) Neurophysiological measures of cognitive workload during human-computer interaction. Theor Issues Ergon Sci 4(1):113

    Article  Google Scholar 

  17. Honal M, Schultz T (2008) Determine task demand from brain activity

  18. Larsson S, Traum D (2000) Information state and dialogue management in the TRINDI dialogue move engine toolkit. Nat Lang Eng 6(3&4):323

    Article  Google Scholar 

  19. Schröder M, Trouvain J (2003) The German text-to-speech synthesis system Mary: a tool for research, development and teaching. Int J Speech Technol 6(4):365

    Article  Google Scholar 

  20. Heger D, Putze F, Schultz T (2010) Online workload recognition from EEG data during cognitive tests and human-machine interaction. In: 33rd annual German conference on artificial intelligence (KI2010), pp 410–417

    Google Scholar 

  21. Jasper H (1958) The 10–20 electrode system of the international federation. Electroencephalogr Clin Neurophysiol 10:371

    Google Scholar 

  22. Heger D, Putze F, Amma C, Wand M, Plotkin I, Wielatt T, Schultz T (2010) BiosignalsStudio: a flexible framework for biosignal capturing and processing. In: 33rd annual German conference on artificial intelligence (KI2010), pp 33–39

    Google Scholar 

  23. Makeig S, Bell A, Jung T, Sejnowski T et al (1996) Independent component analysis of electroencephalographic data. In: Advances in neural information processing systems, pp 145–151

    Google Scholar 

  24. De Clercq W, Vergult A, Vanrumste B, Van Paesschen W, Van Huffel S (2006) Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans Biomed Eng 53(12):2583

    Article  Google Scholar 

  25. Chang C, Lin C (2001) LIBSVM: a library for support vector machines

  26. Asfour T, Welke K, Azad P, Ude A, Dillmann R (2008) The Karlsruhe humanoid head. In: 8th IEEE-RAS international conference on humanoid robots (humanoids), pp 447–453

    Chapter  Google Scholar 

  27. Eriksen C, Schultz D (1979) Information processing in visual search: a continuous flow conception and experimental results. Atten Percept Psychophys 25(4):249

    Article  Google Scholar 

  28. Hart S, Staveland L (1988) Human mental workload. In: Development of NASA-TLX (task load index), pp 139–183

    Google Scholar 

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Authors and Affiliations

  1. Institute for Anthropomatics, Cognitive Systems Lab (CSL), Karlsruhe Institute of Technology, Adenauerring 4, 76131, Karlsruhe, Germany

    Dominic Heger, Felix Putze & Tanja Schultz

Authors
  1. Dominic Heger
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  2. Felix Putze
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  3. Tanja Schultz
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Corresponding author

Correspondence to Dominic Heger.

Additional information

This work has been supported in part by the Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center 588 “Humanoid Robots—Learning and Cooperating Multimodal Robots”.

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Heger, D., Putze, F. & Schultz, T. An EEG Adaptive Information System for an Empathic Robot. Int J of Soc Robotics 3, 415–425 (2011). https://doi.org/10.1007/s12369-011-0107-x

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  • DOI: https://doi.org/10.1007/s12369-011-0107-x

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