skip to main content
10.1145/1719970.1719972acmconferencesArticle/Chapter ViewAbstractPublication PagesiuiConference Proceedingsconference-collections
research-article

A POMDP approach to P300-based brain-computer interfaces

Published: 07 February 2010 Publication History

Abstract

Most of the previous work on non-invasive brain-computer interfaces (BCIs) has been focused on feature extraction and classification algorithms to achieve high performance for the communication between the brain and the computer. While significant progress has been made in the lower layer of the BCI system, the issues in the higher layer have not been sufficiently addressed. Existing P300-based BCI systems, for example the P300 speller, use a random order of stimulus sequence for eliciting P300 signal for identifying users' intentions. This paper is about computing an optimal sequence of stimulus in order to minimize the number of stimuli, hence improving the performance. To accomplish this, we model the problem as a partially observable Markov decision process (POMDP), which is a model for planning in partially observable stochastic environments. Through simulation and human subject experiments, we show that our approach achieves a significant performance improvement in terms of the success rate and the bit rate.

References

[1]
Bander, J.L. and White b, C.C. Markov decision processes with noise-corrupted and delayed state observations. J. Operational Research Society 50 (1999).
[2]
Biopac System Inc. http://www.biopac.com
[3]
Bell, C.J., Shenoy, P., Chalodhorn, R. and Rao, R.P.N. Control of a humanoid robot by a noninvasive brain-computer interface in humans. J. Neural Eng. 5 (2008).
[4]
Doshi, F., Pineau, J. and Roy, N. Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs. Int. Conf. on Machine Learning (2008).
[5]
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R. and Lin, C.J. LIBLINEAR - A library for large linear classification. (2008), http://www.csie.ntu.edu.tw/~cjlin/liblinear/
[6]
Farwell, L.A. and Donchin, E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol 70 (1988).
[7]
Fazel-Rezai, R. Human error in P300 speller paradigm for brain-computer interface. Proc. 29th Annual Int. Conf. of the IEEE EMBS. (2007).
[8]
Fazel-Rezai, R. and Peters, J.F. P300 wave feature extraction: preliminary results. Proc. 18th Annual Canadian Conf. on Electrical and Computer Eng. (2005).
[9]
Hoey J., Poupart, P., von Bertoldi, A., Craig, T., Boutilier, C. and Mihailidis, A. Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process. Computer Vision and Image Understanding (2009).
[10]
Hoffmann, U., Vesin, J.M. and Ebrahimi, T. Spatial filters for the classification of event-related potentials. Proc. 14th ESANN (2006).
[11]
Kaelbling, L.P., Littman, M.L. and Cassandra, A.R. Planning and acting in partially observable stochastic domains. Artificial Intelligence 101 (1998).
[12]
Kanwisher N.G. Repetition blindness: Type recognition without token individuation. Cognition 27 (1987).
[13]
Krusienski, D.J., Sellers, E.W., Cabestaing, F., Bayoudh, S., McFaland, D.J., Vaughan, T.M. and Wolpaw, J.R. A comparison of classification techniques for the P300 Speller. J. Neural Eng. 3 (2006).
[14]
Krusienski, D.J., Sellers, E.W., McFaland, D.J., Vaughan, T.M. and Wolpaw, J.R. Toward enhanced P300 speller performance. J. of Neuroscience Methods 167 (2008).
[15]
Mason, S.G., Bashashati, A., Fatourechi, M., Navarro, K.F. and Birch, G.E. A comprehensive survey of brain interface technology designs. Annals of Biomedical Engineering 35, 2 (2007).
[16]
Pineau, J., Gordon, G. and Thrun, S. Anytime point-based approximations for large POMDPs. J. Artificial Intelligence Research 27 (2006).
[17]
Ramanna, S. and Fazel-Rezai, R. P300 wave detection based on rough sets. Lecture Notes in Computer Science 4100, Springer (2006).
[18]
Serby, H., Yom-Tov, E. and Inbar, G.F. An improved P300-based brain-computer interface. IEEE Trans. Neural. Syst. Rehabil. Eng. 13 (2005).
[19]
Smith, T. and Simmons, R. Point-based POMDP algorithms: Improved analysis and implementations. Proc. 21st Conf. on Uncertainty in Artificial Intelligence (2005).
[20]
Wikipedia. http://en.wikipedia.org/wiki/Electroencephalography
[21]
Wolpaw, J.R., Birbaumer, N., Heetderks, W.J., McFarland, D.J., Peckham, P.H., Schalk, G., Donchin E., Quatrano, L.A., Robinson C.J. and Vaughan, T.M. Brain-computer interface technology: A review of the first international meeting, IEEE Trans. Rehabil. Eng. 8 (2000).
[22]
Wolpaw, J.R., Birbaumer, N., McFarland, D.J., Pfurtscheller, G. and Vaughan, T.M. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113 (2002).
[23]
Williams, J.D. and Young, S. Partially observable Markov decision processes for spoken dialog systems. J. Computer Speech and Language 21 (2007).

Cited By

View all
  • (2024)POMDP-BCI: A Benchmark of (Re)Active BCI Using POMDP to Issue CommandsIEEE Transactions on Biomedical Engineering10.1109/TBME.2023.331857871:3(792-802)Online publication date: Mar-2024
  • (2022)Understanding HCI Practices and Challenges of Experiment Reporting with Brain Signals: Towards Reproducibility and ReuseACM Transactions on Computer-Human Interaction10.1145/349055429:4(1-43)Online publication date: 31-Mar-2022
  • (2016)Exploiting EEG Channel Correlations in P300 Speller Paradigm for Brain-Computer InterfaceIEICE Transactions on Information and Systems10.1587/transinf.2014EDP7399E99.D:6(1653-1662)Online publication date: 2016
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
IUI '10: Proceedings of the 15th international conference on Intelligent user interfaces
February 2010
460 pages
ISBN:9781605585154
DOI:10.1145/1719970
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2010

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. P300
  2. brain-computer interface (bci)
  3. partially observable markov decision process (pomdp)

Qualifiers

  • Research-article

Conference

IUI '10
Sponsor:

Acceptance Rates

Overall Acceptance Rate 746 of 2,811 submissions, 27%

Upcoming Conference

IUI '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)14
  • Downloads (Last 6 weeks)0
Reflects downloads up to 14 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)POMDP-BCI: A Benchmark of (Re)Active BCI Using POMDP to Issue CommandsIEEE Transactions on Biomedical Engineering10.1109/TBME.2023.331857871:3(792-802)Online publication date: Mar-2024
  • (2022)Understanding HCI Practices and Challenges of Experiment Reporting with Brain Signals: Towards Reproducibility and ReuseACM Transactions on Computer-Human Interaction10.1145/349055429:4(1-43)Online publication date: 31-Mar-2022
  • (2016)Exploiting EEG Channel Correlations in P300 Speller Paradigm for Brain-Computer InterfaceIEICE Transactions on Information and Systems10.1587/transinf.2014EDP7399E99.D:6(1653-1662)Online publication date: 2016
  • (2016)User-centered gesture development in TV viewing environmentMultimedia Tools and Applications10.1007/s11042-014-2323-575:2(733-760)Online publication date: 1-Jan-2016
  • (2015)Quantitative Evaluation of a Low-Cost Noninvasive Hybrid Interface Based on EEG and Eye MovementIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2014.236583423:2(159-168)Online publication date: Mar-2015
  • (2015)Increasing BCI communication rates with dynamic stopping towards more practical use: an ALS studyJournal of Neural Engineering10.1088/1741-2560/12/1/01601312:1(016013)Online publication date: 14-Jan-2015
  • (2015)ALS Population Assessment of a Dynamic Stopping Algorithm Implementation for P300 SpellersBrain-Computer Interface Research10.1007/978-3-319-25190-5_8(79-87)Online publication date: 13-Dec-2015
  • (2014)BMI-based framework for teaching and evaluating robot skills2014 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA.2014.6907749(6040-6046)Online publication date: May-2014
  • (2014)Wearable wireless interface based on brain activity and eye movement2014 14th International Conference on Control, Automation and Systems (ICCAS 2014)10.1109/ICCAS.2014.6988004(286-289)Online publication date: Oct-2014
  • (2014)Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye trackingComputers in Biology and Medicine10.1016/j.compbiomed.2014.04.02051(82-92)Online publication date: 1-Aug-2014
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media