Skip to main content

MATLAB-Based Tools for BCI Research

  • Chapter

Part of the book series: Human-Computer Interaction Series ((HCIS))

Abstract

We first discuss two MATLAB-centered solutions for real-time data streaming, the environments FieldTrip (Donders Institute, Nijmegen) and DataSuite (Data- River, Producer, MatRiver) (Swartz Center, La Jolla). We illustrate the relative simplicity of coding BCI feature extraction and classification under MATLAB (The Mathworks, Inc.) using a minimalist BCI example, and then describe BCILAB (Team PhyPa, Berlin), a new BCI package that uses the data structures and extends the capabilities of the widely used EEGLAB signal processing environment. We finally review the range of standalone and MATLAB-based software currently freely available to BCI researchers.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   149.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Adapt © 1987–2003 and Varieté, © 2000, 2001 are property of EEG Solutions LLC, and are used under free license for scientific non-profit research

    Google Scholar 

  • Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159

    Article  Google Scholar 

  • Babiloni F, et al. (1995) Performances of surface Laplacian estimators: A study of simulated and real scalp potential distributions. Brain Topogr 8(1):35–45

    Article  Google Scholar 

  • Bilmes J (1998) Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. International Computer Science Institute

    Google Scholar 

  • Birbaumer N, et al. (2009) Neurofeedback and brain-computer interface clinical applications. Int Rev Neurobiol 86:107–187

    Article  Google Scholar 

  • Blankertz B, Curio G, Müller K (2002a) Classifying single trial EEG: Towards brain computer interfacing. In: Diettrich T, Becker S, Ghahramani Z (eds) Advances in Neural Inf Proc Systems (NIPS 01), pp 157–164

    Google Scholar 

  • Blankertz B, et al (2002b) Single trial detection of EEG error potentials: A tool for increasing BCI transmission rates. In: Artificial Neural Networks—ICANN 2002

    Google Scholar 

  • Delorme A, Makeig S (2004) EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

    Article  Google Scholar 

  • Dornhege G, Blankertz B, Curio G (2003a) Speeding up classification of multi-channel brain-computer interfaces: Common spatial patterns for slow cortical potentials. In: First International IEEE EMBS Conference on In Neural Engineering

    Google Scholar 

  • Dornhege G, et al (2003b) Combining features for BCI, In: Becker S, Thrun S, Obermayer K (eds), Proc Systems (NIPS 02), pp 1115–1122

    Google Scholar 

  • Farwell L, Donchin E (1988) Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 70(6):510–523

    Article  Google Scholar 

  • Figueiredo M, Jain A (2002) Unsupervised learning on finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3)

    Google Scholar 

  • Fisher R (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7:179–188

    Article  Google Scholar 

  • Friedman J (2002) Regularized discriminant analysis. J Am Stat Assoc 84(405):165–175

    Article  Google Scholar 

  • Hinton G, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  • Jaakkola T, Jordan M (1997) A variational approach to Bayesian logistic regression models and their extensions. In: Sixth International Workshop on Artificial Intelligence and Statistics

    Google Scholar 

  • Jung T-P, et al. (1997) Estimating alertness from the EEG power spectrum. IEEE Trans Biomed Eng 44(1):60–69

    Article  Google Scholar 

  • Kothe C (2009) Design and Implementation of a Research Brain-Computer Interface. Berlin Institute of Technology, Berlin. Section 8.2.1

    Google Scholar 

  • Lin C-T, et al. (2008) A noninvasive prosthetic platform using mobile & wireless EEG. Proc IEEE 96(7):1167–1183

    Article  Google Scholar 

  • Makeig S, et al. (2009) Linking brain, mind and behavior. Int J Psychophysiol 73(2):95–100

    Article  Google Scholar 

  • Makeig S, et al. (2002) Dynamic brain sources of visual evoked responses. Science 295(5555):690–694

    Article  Google Scholar 

  • Makeig S, Inlow M (1993) Lapses in alertness: Coherence of fluctuations in performance and EEG spectrum. Electroencephalogr Clin Neurophysiol 86(1):23–35

    Article  Google Scholar 

  • Makeig S, et al (1996) Independent component analysis of electroencephalographic data. In: Touretzky D, Mozer M, Hasselmo M (eds), Advances in Neural Information Processing Systems, pp 145–151

    Google Scholar 

  • Miner LA, McFarland DJ, Wolpaw JR (1998) Answering questions with an electroencephalogram-based brain-computer interface. Arch Phys Med Rehabil 79(9):1029–1033

    Article  Google Scholar 

  • Palmer JA, et al (2007) Modeling and estimation of dependent subspaces with non-radially symmetric and skewed densities. In: 7th International Conference on Independent Component Analysis and Signal Separation, London, UK

    Google Scholar 

  • Perrin F et al. (1987) Mapping of scalp potentials by surface spline interpolation. Electroencephalogr Clin Neurophysiol 66(1):75–81

    Article  MathSciNet  Google Scholar 

  • Ramoser H, Müller-Gerking J, Pfurtscheller G (1998) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 1998(8):441–446

    Google Scholar 

  • Schalk G, et al. (2004) BCI2000: A general-purpose brain-computer interface (BCI) system. IEEE Trans Biomed Eng 51(6):1034–1043

    Article  Google Scholar 

  • Schlögl A (2000) The electroencephalogram and the Adaptive Autoregressive Model: Theory and Applications. Shaker Verlag, Aachen. ISBN3-8265-7640-3

    Google Scholar 

  • Schlögl A, Brunner C (2000) Biosig: A free and open source software library for BCI research. Computer 41(10):44–50

    Article  Google Scholar 

  • Schölkopf B, Smola A (2002) Learning with Kernels. MIT Press, Cambridge, MA

    Google Scholar 

  • Sellers E, et al. (2006) A p300 event-related potential brain-computer interface (BCI): The effects of matrix size and inter stimulus interval on performance. Biol Psychol 73(3):242–252

    Article  Google Scholar 

  • Tipping M (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–244

    MathSciNet  MATH  Google Scholar 

  • Tomioka R et al. (2006) An iterative algorithm for spatio-temporal filter optimization. In: 3rd International BCI Workshop and Training Course. Verlag der Technischen Universität Graz, Graz

    Google Scholar 

  • Venthur B, Blankertz B (2008) A platform-independent open-source feedback framework for BCI systems. In: 4th International Brain-Computer Interface Workshop and Training Course

    Google Scholar 

  • Vidaurre C, Schlögl (2008) A comparison of adaptive features with linear discriminant classifier for brain computer interfaces. In: Engineering in Medicine and Biology Society. EMBS 2008. 30th Annual International Conference of the IEEE

    Google Scholar 

  • Vlassis N, Likas A, Greedy EM (2002) Algorithm for Gaussian Mixture Learning. Neural Processing Letters, vol 15. Kluwer Academic Publishers, Dordrecht

    Google Scholar 

  • Zander T, Jatzev S (2009) Detecting affective covert user states with passive brain-computer interfaces. In: ACII 2009. IEEE Computer Society Press, Los Alamitos, CA

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arnaud Delorme .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag London Limited

About this chapter

Cite this chapter

Delorme, A. et al. (2010). MATLAB-Based Tools for BCI Research. In: Tan, D., Nijholt, A. (eds) Brain-Computer Interfaces. Human-Computer Interaction Series. Springer, London. https://doi.org/10.1007/978-1-84996-272-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-1-84996-272-8_14

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84996-271-1

  • Online ISBN: 978-1-84996-272-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics