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Biomedical Engineering / Biomedizinische Technik

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Volume 61, Issue 1 (Feb 2016)

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Volume 57 (2012)

Random forests in non-invasive sensorimotor rhythm brain-computer interfaces: a practical and convenient non-linear classifier

David Steyrl
  • Institute for Knowledge Discovery, BCI Lab, Graz University of Technology, Graz, Austria
/ Reinhold Scherer
  • Corresponding author
  • Institute for Knowledge Discovery, BCI Lab, Graz University of Technology, Graz, Austria
  • Email:
/ Josef Faller
  • Biological Psychology and Neuroergonomics, Berlin Institute of Technology, Berlin, Germany
/ Gernot R. Müller-Putz
  • Institute for Knowledge Discovery, BCI Lab, Graz University of Technology, Graz, Austria
Published Online: 2015-04-01 | DOI: https://doi.org/10.1515/bmt-2014-0117

Abstract

There is general agreement in the brain-computer interface (BCI) community that although non-linear classifiers can provide better results in some cases, linear classifiers are preferable. Particularly, as non-linear classifiers often involve a number of parameters that must be carefully chosen. However, new non-linear classifiers were developed over the last decade. One of them is the random forest (RF) classifier. Although popular in other fields of science, RFs are not common in BCI research. In this work, we address three open questions regarding RFs in sensorimotor rhythm (SMR) BCIs: parametrization, online applicability, and performance compared to regularized linear discriminant analysis (LDA). We found that the performance of RF is constant over a large range of parameter values. We demonstrate – for the first time – that RFs are applicable online in SMR-BCIs. Further, we show in an offline BCI simulation that RFs statistically significantly outperform regularized LDA by about 3%. These results confirm that RFs are practical and convenient non-linear classifiers for SMR-BCIs. Taking into account further properties of RFs, such as independence from feature distributions, maximum margin behavior, multiclass and advanced data mining capabilities, we argue that RFs should be taken into consideration for future BCIs.

Keywords: brain-computer interfaces; machine learning; random forests; regularized linear discriminant analysis; sensorimotor rhythms

References

  • [1]

    Akram F, Han HS, Jeon HJ, Park K, Park S-H, Cho J, Kim T-S. An efficient words typing P300-BCI system using a modified T9 interface and random forest classifier. Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp.2251–2254, 3-7 July 2013.

  • [2]

    AlZoubi O, Koprinska I, Calvo RA. Classification of brain-computer interface data. In Proc. Seventh Australasian Data Mining Conference (AusDM 2008), Glenelg, South Australia. CRPIT, 87. Roddick JF, Li J, Christen P, Kennedy PJ, editors. ACS 2008: 123–131.

  • [3]

    Ang KK, Chin ZY, Wang C, Guan C, Zhang H. Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b. Front Neurosci 2012; 6: 39. [Web of Science] [Crossref]

  • [4]

    Barbero A, Grosse-Wentrup M. Biased feedback in brain-computer interfaces. J Neuroeng Rehabil 2010; 7: 1–4. [Crossref] [Web of Science]

  • [5]

    Bentlemsan M, Zemouri E. Random forest and filter bank common spatial patterns for eeg-based motor imagery classification. Proceeding of the 5th International Conference on Intelligent System Modeling and Simulation 2014 (ISMS 2014).

  • [6]

    Birbaumer N, Ghanayim N, Hinterberger T, et al. A spelling device for the paralysed. Nature 1999; 398: 297–298.

  • [7]

    Blankertz B, Lemm S, Treder M, Haufe S, Müller KR. Single-trial analysis and classification of ERP components – a tutorial. Neuroimage 2011; 56: 814–825. [Web of Science] [Crossref]

  • [8]

    Blankertz B, Losch F, Krauledat M, Dornhege G, Curio G, Müller KR. The berlin brain-computer interface: accurate performance from first-session in BCI-naive subjects. IEEE Trans Biomed Eng 2008; 55: 2452–2462. [Crossref]

  • [9]

    Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR. Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 2008; 25: 41–56. [Web of Science] [Crossref]

  • [10]

    Breiman L. Bagging predictors. Mach Learn 1996; 24: 123–140. [Crossref]

  • [11]

    Breiman L. Random forests. Mach Learn 2001; 45: 5–32. [Crossref]

  • [12]

    Breiman L, Friedman JH, Olshen RA, Stone CJ. CART: classification and regression trees. Wadsworth: Belmont 1983.

  • [13]

    Breitwieser C, Daly I, Neuper C, Müller-Putz GR. Proposing a standardized protocol for raw biosignal transmission. IEEE Trans Biomed Eng 2012; 59: 852–859. [Web of Science] [Crossref]

  • [14]

    Criminisi A, Shotton J, Konukoglu E. Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning. Microsoft Research Technical Report: TR-2011-114.

  • [15]

    Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage 2007; 34: 1443–1449. [Web of Science] [Crossref]

  • [16]

    Faller J, Vidaurre C, Solis-Escalante T, Neuper C, Scherer R. Autocalibration and recurrent adaptation: towards a plug and play online ERD-BCI. IEEE Trans Neural Syst Rehabil Eng 2012; 20: 313–319. [Web of Science] [Crossref]

  • [17]

    Farooq F, Kidmose P. Random forest classification for P300 based brain computer interface applications. Proceedings of the 21st European Signal Processing Conference (EUSIPCO) 2013.

  • [18]

    Hjorth B. An on-line transformation of EEG scalp potentials into orthogonal source derivations. Electroen Clin Neuro 1975; 39: 526–530. [Crossref]

  • [19]

    Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell 1998; 20: 832–844. [Crossref]

  • [20]

    Jaiantilal A. Random forest implementation for MATLAB. Available from: http://code.google.com/p/randomforest-matlab/. Accessed 6 November, 2012.

  • [21]

    Jatzev S, Zander TO, DeFilippis M, Kothe C, Welke S, Roetting M. Examining causes for non-stationarities: The loss of controllability is a factor which induces non-stationarities. Proc. of the 4th International Brain-Computer Interface Workshop and Training Course 2008 (Graz, Austria). Graz: Verlag der Technischen Universität 2008: 138–143.

  • [22]

    Kreilinger A, Kaiser V, Rohm M, Leeb R, Rupp R, Müller-Putz GR. Neuroprosthesis control via noninvasive hybrid brain-computer interface. IEEE Intell Syst 2014; 28: 40–43.

  • [23]

    Kübler A, Furdea A, Halder S, Hammer EM, Nijboer F, Kotchoubey B. A brain-computer interface controlled auditory event-related potential (P300) spelling system for locked-in patients. Ann NY Acad Sci 2009; 1157: 90–100. [Web of Science]

  • [24]

    Kübler A, Neumann N, Kaiser J, Kotchoubey B, Hinterberger T, Birbaumer N. Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. Arch Phys Med Rehabil 2001; 82: 1533–1539. [Crossref]

  • [25]

    Kübler A, Nijboer F, Mellinger J, et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 2005; 64: 1775–1777. [Crossref]

  • [26]

    Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG-based brain-computer interfaces. J Neural Eng 2007; 4: 1–13. [Crossref]

  • [27]

    Meyer D, Leisch F, Hornik K. The support vector machine under test. Neurocomputing 2003; 55: 169–186. [Crossref]

  • [28]

    Millán JD, Rupp R, Müller-Putz GR, et al. Combining brain-computer interfaces and assistive technologies: state-of-the-art and challenges. Front Neurosci 2010; 4: 161.

  • [29]

    Müller KR, Anderson CW, Birch GE. Linear and nonlinear methods for brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng 2003; 11: 165–169. [Crossref]

  • [30]

    Müller-Putz GR, Scherer R, Brunner C, Leeb R, Pfurtscheller G. Better than random? A closer look on BCI results. Int J Bioelectromagn 2008; 10: 52–55.

  • [31]

    Müller-Putz GR, Scherer R, Pfurtscheller G, Neuper C. Temporal coding of brain patterns for direct limb control in humans. Front Neurosci 2010; 4: 34.

  • [32]

    Müller-Putz GR, Scherer R, Pfurtscheller G, Rupp R. EEG-based neuroprosthesis control: a step towards clinical practice. Neurosci Lett 2005; 382: 169–174.

  • [33]

    Neuper C, Scherer R, Reiner M, Pfurtscheller G. Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. Cogn Brain Res 2005; 25: 668–677. [Crossref]

  • [34]

    Nijboer F, Sellers EW, Mellinger J, et al. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 2008; 119: 1909–1916. [Crossref] [Web of Science]

  • [35]

    Pfurtscheller G, Neuper C. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 1997; 239: 65–66.

  • [36]

    Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proc IEEE 2001; 89: 1123–1134. [Crossref]

  • [37]

    Pfurtscheller G, Allison BZ, Brunner C, et al. The hybrid BCI. Front Neurosci 2010; 4: 30.

  • [38]

    Pfurtscheller G, Müller GR, Pfurtscheller J, Gerner HJ, Rupp R. ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett 2003; 351: 33–36.

  • [39]

    Pokorny C, Klobassa D, Pichler G, et al. The auditory P300-based single-switch brain-computer interface: Paradigm transition from healthy subjects to minimally conscious patients. Artif Intel Med 2013; 59: 81–90.

  • [40]

    Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehab Eng 2000; 8: 441–446. [Crossref]

  • [41]

    Scherer R, Pfurtscheller G, Neuper C. Motor imagery induced changes in oscillatory EEG components: speed vs. accuracy. Proc of the 4th International Brain-Computer Interface Workshop and Training Course 2008 (Graz, Austria). Graz: Verlag der Technischen Universität Graz 2008: 186–190.

  • [42]

    Steyrl D, Scherer R, Müller-Putz GR. Random forests for feature selection in non-invasive brain-computer interfacing. In: Holzinger A, Pasi G, editors. Human-computer interaction and knowledge discovery in complex, unstructured, big data, lecture notes in computer science. 2013; 7947: 207–216.

  • [43]

    Steyrl D, Scherer R, Müller-Putz GR. Using random forests for classifying motor imagery EEG. Proceedings of TOBI workshop IV 2013; 89–90.

  • [44]

    Sun S, Zhang C, Zhang D. An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognit Lett 2007; 28: 2157–2163.

  • [45]

    Verikas A, Gelzinis A, Bacauskiene M. Mining data with random forests: a survey and results of new tests. Pattern Recognit 2011; 44: 330–349. [Web of Science] [Crossref]

  • [46]

    Weichwald S, Meyer T, Schölkopf B, Ball T, Grosse-Wentrup M. Decoding index finger position from EEG using random forests. Fourth International Workshop on Cognitive Information Processing, Copenhagen, Denmark 2014.

  • [47]

    Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002; 113: 767–791. [Crossref]

  • [48]

    Wolpaw JR, Winter Wolpaw E. Brain-computer interfaces: something new under the sun. In: Wolpaw JR, Winter Wolpaw E, editors. Brain-computer interfaces: principles and practice. New York: Oxford University Press 2012: 3–12.

About the article

Corresponding author: Reinhold Scherer, Institute for Knowledge Discovery, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Inffeldgasse 13/IV, 8010 Graz, Austria, Phone: +43 316 873 30713, Fax: +43 316 873 30702, E-mail:


Received: 2014-10-03

Accepted: 2015-03-02

Published Online: 2015-04-01

Published in Print: 2016-02-01



Citation Information: Biomedical Engineering / Biomedizinische Technik, ISSN (Online) 1862-278X, ISSN (Print) 0013-5585, DOI: https://doi.org/10.1515/bmt-2014-0117. Export Citation

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