Abstract
The detection of event related potentials and their usage for innovative applications became an increasingly important research topic for brain computer interfaces in the last couple of years. However, brain computer interfaces use methods that need to be trained on subject-specific data before they can be used. This problem must be solved for real-world applications in which humans are multi tasking and hence are to some degree are less predictable in their behavior compared to classical set ups for brain computer interfacing. In this paper, we show the detection and passive usage of the P300 related brain activity in a highly uncontrolled and noisy application scenario. The subjects are multi tasking, i.e., they perform a demanding senso-motor task, i.e., the telemanipulate a real robotic arm while responding to important messages. For telemanipulation, the subject wears an active exoskeleton to control a robotic arm, which is presented to him in a virtual scenario. By online analysis of the subject’s electroencephalogram we detect P300 related target recognition processes to infer on upcoming response behavior on presented task-relevant messages (Targets) or missing of response behavior in case a Target was not recognized. We show that a classifier that is trained to distinguish between brain activity evoked by recognized task-relevant stimuli (recognized Targets) and ignored frequent task-irrelevant stimuli (Standards) can be applied to classify between brain activity evoked by recognized targets and brain activity that is evoked in case that task-relevant stimuli are not recognized (Missed Targets). The applied transfer of classifier results in reduced performance. We show that this draw back of the approach can strongly be improved by using online machine learning tools to adapt the pre-trained classifier to the new class, i.e., to the Missed Target class, that was not used during training of the classifier.
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Notes
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Available at http://pyspace.github.com/pyspace.
References
Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res. 7, 551–585 (2006)
Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510–523 (1988)
Folgheraiter, M., Jordan, M., Straube, S., Seeland, A., Kim, S.-K., Kirchner, E.A.: Measuring the improvement of the interaction comfort of a wearable exoskeleton. Int. J. Soc. Robotics 4(3), 285–302 (2012)
George, L., Lécuyer, A.: An overview of research on “passive” brain-computer interfaces for implicit human-computer interaction. In: International Conference on Applied Bionics and Biomechanics ICABB 2010 - Workshop W1 “Brain-Computer Interfacing and Virtual Reality”. Venice, Italy (2010)
Guger, C., Harkam, W., Hertnaes, C., Pfurtscheller, G.: Prosthetic control by an EEG-based brain-computer interface (BCI). In: 5th European AAATE Conference (1999)
Haufe, S., Treder, M.S., Gugler, M.F., Sagebaum, M., Curio, G., Blankertz, B.: EEG potentials predict upcoming emergency brakings during simulated driving. J. Neural Eng. 8(5), 066003 (2011)
Iturrate, I., Montesano, L., Minguez, J.: task-dependent signal variations in EEG error-related potentials for brain-computer interfaces. J. Neural Eng. 10, 026024 (2013)
Kim, S.K., Kirchner, E.A.: Classifier transferability in the detection of error related potentials from observation to interaction. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013, Manchester, UK, 13–16 October 2013 (2013)
Kirchner, E.A., Metzen, J.H., Duchrow, T., Kim, S.K., Kirchner, F.: Assisting telemanipulation operators via real-time brain reading. In: Lohweg, V., Niggemann, O. (eds.) Proceedings of Machine Learning in Real-time Application Workshop 2009. Lemgoer Schriftenreihe zur industriellen Informationstechnik, Paderborn, Germany (2009)
Kirchner, E.A., Wöhrle, H., Bergatt, C., Kim, S.K., Metzen, J.H., Feess, D., Kirchner, F.: Towards operator monitoring via brain reading - an EEG-based approach for space applications. In: Proceedings of 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space, Sapporo (2010)
Kirchner, E.A., Drechsler, R.: A Formal Model for Embedded Brain Reading, vol. 40. Emerald Group Publishing Limited, Bingley (2013)
Kirchner, E.A., Kim, S.K.: EEG in dual-task human-machine interaction: target recognition and prospective memory. In: Proceedings of the 18th Annual Meeting of the Organization for Human Brain Mapping (2012)
Kirchner, E.A., Kim, S.K., Straube, S., Seeland, A., Wöhrle, H., Krell, M.M., Tabie, M., Fahle, M.: On the applicability of brain reading for predictive human-machine interfaces in robotics. PLoS ONE 8, e81732 (2013)
Krell, M.M., Straube, S., Seeland, A., Wöhrle, H., Teiwes, J., Metzen, J.H., Kirchner, E.A., Kirchner, F.: pySPACE (2013). https://github.com/pyspace
Krell, M.M., Straube, S., Seeland, A., Wöhrle, H., Teiwes, J., Metzen, J.H., Kirchner, E.A., Kirchner, F.: pySPACE - a signal processing and classification environment in Python. Front. Neuroinf. 7(40) (2013)
Kutas, M., McCarthy, G., Donchin, E.: Augmenting mental chronometry: the P300 as a measure of stimulus evaluation time. Science 197(4305), 792–795 (1977)
Nijholt, A., Tan, D., Allison, B.Z., Del R Milan, J., Graimann, B.: Brain-Computer Interfaces for HCI and Games. ACM (2008)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Polich, J.: Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118(10), 2128–2148 (2007)
Reuderink, B.: Games and Brain-Computer Interfaces: The State of the Art. WP2 BrainGain Deliverable HMI University of Twente September 2008 (2008)
Rivet, B., Souloumiac, A., Attina, V., Gibert, G.: xDAWN algorithm to enhance evoked potentials: application to brain-computer interface. IEEE Trans. Biomed. Eng. 56(8), 2035–2043 (2009)
Salisbury, D.F., Rutherford, B., Shenton, M.E., McCarley, R.W.: Button-pressing affects P300 amplitude and scalp topography. Clin. Neurophysiol. 112(9), 1676–1684 (2001)
Seeland, A., Wöhrle, H., Straube, S., Kirchner, E.A.: Online movement prediction in a robotic application scenario. In: 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER) (2013)
Straube, S., Krell, M.M.: How to evaluate an agent’s behaviour to infrequent events? – Reliable performance estimation insensitive to class distribution. Front. Comput. Neurosci. 8(43) (2014)
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G., Vaughan, T.M.: Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113(6), 767–791 (2002)
Zander, T.O., Kothe, C., Jatzev, S., Gaertner, M.: Enhancing human-computer interaction with input from active and passive brain-computer interfaces. Brain-Computer Interfaces (2010)
Acknowledgements
This work was funded by the Federal Ministry of Economics and Technology (BMWi, grant no. 50 RA 1012 and 50 RA 1011).
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Woehrle, H., Kirchner, E.A. (2014). Online Classifier Adaptation for the Detection of P300 Target Recognition Processes in a Complex Teleoperation Scenario. In: da Silva, H., Holzinger, A., Fairclough, S., Majoe, D. (eds) Physiological Computing Systems. PhyCS 2014. Lecture Notes in Computer Science(), vol 8908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45686-6_7
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