IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0178410.html
   My bibliography  Save this article

Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion

Author

Listed:
  • Raheel Zafar
  • Sarat C Dass
  • Aamir Saeed Malik

Abstract

Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain–computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types of images. In this hybrid algorithm, convolutional neural network is modified for the extraction of features, a t-test is used for the selection of significant features and likelihood ratio-based score fusion is used for the prediction of brain activity. The proposed algorithm takes input data from multichannel EEG time-series, which is also known as multivariate pattern analysis. Comprehensive analysis was conducted using data from 30 participants. The results from the proposed method are compared with current recognized feature extraction and classification/prediction techniques. The wavelet transform-support vector machine method is the most popular currently used feature extraction and prediction method. This method showed an accuracy of 65.7%. However, the proposed method predicts the novel data with improved accuracy of 79.9%. In conclusion, the proposed algorithm outperformed the current feature extraction and prediction method.

Suggested Citation

  • Raheel Zafar & Sarat C Dass & Aamir Saeed Malik, 2017. "Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-23, May.
    • Handle: RePEc:plo:pone00:0178410
    DOI: 10.1371/journal.pone.0178410
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0178410
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0178410&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0178410?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Kendrick N. Kay & Thomas Naselaris & Ryan J. Prenger & Jack L. Gallant, 2008. "Identifying natural images from human brain activity," Nature, Nature, vol. 452(7185), pages 352-355, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Umut Güçlü & Marcel A J van Gerven, 2014. "Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-12, August.
    2. Guillermo A Cecchi & Lejian Huang & Javeria Ali Hashmi & Marwan Baliki & María V Centeno & Irina Rish & A Vania Apkarian, 2012. "Predictive Dynamics of Human Pain Perception," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-12, October.
    3. Zvi N. Roth & Kendrick Kay & Elisha P. Merriam, 2022. "Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Hamed Nili & Cai Wingfield & Alexander Walther & Li Su & William Marslen-Wilson & Nikolaus Kriegeskorte, 2014. "A Toolbox for Representational Similarity Analysis," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-11, April.
    5. Hamed Nili & Alexander Walther & Arjen Alink & Nikolaus Kriegeskorte, 2020. "Inferring exemplar discriminability in brain representations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    6. Jacob M. Paul & Martijn Ackooij & Tuomas C. Cate & Ben M. Harvey, 2022. "Numerosity tuning in human association cortices and local image contrast representations in early visual cortex," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    7. Agustin Lage-Castellanos & Giancarlo Valente & Elia Formisano & Federico De Martino, 2019. "Methods for computing the maximum performance of computational models of fMRI responses," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-25, March.
    8. Kay H Brodersen & Thomas M Schofield & Alexander P Leff & Cheng Soon Ong & Ekaterina I Lomakina & Joachim M Buhmann & Klaas E Stephan, 2011. "Generative Embedding for Model-Based Classification of fMRI Data," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-19, June.
    9. Shinsuke Koyama & Uri Eden & Emery Brown & Robert Kass, 2010. "Bayesian decoding of neural spike trains," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 37-59, February.
    10. Kiyohito Iigaya & Sanghyun Yi & Iman A. Wahle & Sandy Tanwisuth & Logan Cross & John P. O’Doherty, 2023. "Neural mechanisms underlying the hierarchical construction of perceived aesthetic value," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    11. Ming Bo Cai & Nicolas W Schuck & Jonathan W Pillow & Yael Niv, 2019. "Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-30, May.
    12. Ghislain St-Yves & Emily J. Allen & Yihan Wu & Kendrick Kay & Thomas Naselaris, 2023. "Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    13. Hanzhong Liu & Bin Yu, 2017. "Comments on: High-dimensional simultaneous inference with the bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 740-750, December.
    14. Yargholi, E. & Hossein-Zadeh, G.-A., 2019. "Cross recurrence quantifiers as new connectivity measures for structure learning of Bayesian networks in brain decoding," Chaos, Solitons & Fractals, Elsevier, vol. 123(C), pages 263-274.
    15. Jörn Diedrichsen & Nikolaus Kriegeskorte, 2017. "Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-33, April.
    16. Marcel Adam Just & Vladimir L Cherkassky & Augusto Buchweitz & Timothy A Keller & Tom M Mitchell, 2014. "Identifying Autism from Neural Representations of Social Interactions: Neurocognitive Markers of Autism," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-22, December.
    17. Leech, Dennis & Leech, Robert & Simmonds, Anna, 2009. "Parametric inference for functional information mapping," The Warwick Economics Research Paper Series (TWERPS) 899, University of Warwick, Department of Economics.
    18. Johannes Haushofer & Margaret S Livingstone & Nancy Kanwisher, 2008. "Multivariate Patterns in Object-Selective Cortex Dissociate Perceptual and Physical Shape Similarity," PLOS Biology, Public Library of Science, vol. 6(7), pages 1-9, July.
    19. Lauren L Emberson & Benjamin D Zinszer & Rajeev D S Raizada & Richard N Aslin, 2017. "Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-23, April.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0178410. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.