IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v312y2022i2d10.1007_s10479-020-03921-0.html
   My bibliography  Save this article

Improving P300 Speller performance by means of optimization and machine learning

Author

Listed:
  • Luigi Bianchi

    (University of Rome Tor Vergata)

  • Chiara Liti

    (University of Rome Tor Vergata)

  • Giampaolo Liuzzi

    (Sapienza University of Rome)

  • Veronica Piccialli

    (University of Rome Tor Vergata)

  • Cecilia Salvatore

    (University of Rome Tor Vergata)

Abstract

Brain-Computer Interfaces (BCIs) are systems allowing people to interact with the environment bypassing the natural neuromuscular and hormonal outputs of the peripheral nervous system (PNS). These interfaces record a user’s brain activity and translate it into control commands for external devices, thus providing the PNS with additional artificial outputs. In this framework, the BCIs based on the P300 Event-Related Potentials (ERP), which represent the electrical responses recorded from the brain after specific events or stimuli, have proven to be particularly successful and robust. The presence or the absence of a P300 evoked potential within the EEG features is determined through a classification algorithm. Linear classifiers such as stepwise linear discriminant analysis and support vector machine (SVM) are the most used discriminant algorithms for ERPs’ classification. Due to the low signal-to-noise ratio of the EEG signals, multiple stimulation sequences (a.k.a. iterations) are carried out and then averaged before the signals being classified. However, while augmenting the number of iterations improves the Signal-to-Noise Ratio, it also slows down the process. In the early studies, the number of iterations was fixed (no stopping environment), but recently several early stopping strategies have been proposed in the literature to dynamically interrupt the stimulation sequence when a certain criterion is met in order to enhance the communication rate. In this work, we explore how to improve the classification performances in P300 based BCIs by combining optimization and machine learning. First, we propose a new decision function that aims at improving classification performances in terms of accuracy and Information Transfer Rate both in a no stopping and early stopping environment. Then, we propose a new SVM training problem that aims to facilitate the target-detection process. Our approach proves to be effective on several publicly available datasets.

Suggested Citation

  • Luigi Bianchi & Chiara Liti & Giampaolo Liuzzi & Veronica Piccialli & Cecilia Salvatore, 2022. "Improving P300 Speller performance by means of optimization and machine learning," Annals of Operations Research, Springer, vol. 312(2), pages 1221-1259, May.
    • Handle: RePEc:spr:annopr:v:312:y:2022:i:2:d:10.1007_s10479-020-03921-0
    DOI: 10.1007/s10479-020-03921-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-020-03921-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-020-03921-0?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wanpracha Chaovalitwongse & Oleg Prokopyev & Panos Pardalos, 2006. "Electroencephalogram (EEG) time series classification: Applications in epilepsy," Annals of Operations Research, Springer, vol. 148(1), pages 227-250, November.
    2. Anahita Khojandi & Oleg Shylo & Maryam Zokaeinikoo, 2019. "Automatic EEG classification: a path to smart and connected sleep interventions," Annals of Operations Research, Springer, vol. 276(1), pages 169-190, May.
    3. Veronica Piccialli & Marco Sciandrone, 2018. "Nonlinear optimization and support vector machines," 4OR, Springer, vol. 16(2), pages 111-149, June.
    4. Riccardo Poli & Davide Valeriani & Caterina Cinel, 2014. "Collaborative Brain-Computer Interface for Aiding Decision-Making," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-22, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Praveen Puram & Soumya Roy & Deepak Srivastav & Anand Gurumurthy, 2023. "Understanding the effect of contextual factors and decision making on team performance in Twenty20 cricket: an interpretable machine learning approach," Annals of Operations Research, Springer, vol. 325(1), pages 261-288, June.

    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. Yves Crama & Michel Grabisch & Silvano Martello, 2022. "Preface," Annals of Operations Research, Springer, vol. 314(1), pages 1-3, July.
    2. Veronica Piccialli & Marco Sciandrone, 2022. "Nonlinear optimization and support vector machines," Annals of Operations Research, Springer, vol. 314(1), pages 15-47, July.
    3. Yves Crama & Michel Grabisch & Silvano Martello, 2021. "4OR comes of age," 4OR, Springer, vol. 19(1), pages 1-13, March.
    4. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
    5. Wanpracha Chaovalitwongse, 2009. "Comments on: Optimization and data mining in medicine," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(2), pages 247-249, December.
    6. Laura Palagi, 2019. "Global optimization issues in deep network regression: an overview," Journal of Global Optimization, Springer, vol. 73(2), pages 239-277, February.
    7. Wanpracha Art Chaovalitwongse, 2008. "Novel quadratic programming approach for time series clustering with biomedical application," Journal of Combinatorial Optimization, Springer, vol. 15(3), pages 225-241, April.
    8. W. Art Chaovalitwongse & Ya-Ju Fan & Rajesh C. Sachdeo, 2008. "Novel Optimization Models for Abnormal Brain Activity Classification," Operations Research, INFORMS, vol. 56(6), pages 1450-1460, December.
    9. Yerim Choi & Jonghun Park & Dongmin Shin, 2017. "A semi-supervised inattention detection method using biological signal," Annals of Operations Research, Springer, vol. 258(1), pages 59-78, November.
    10. Ya-Ju Fan & Wanpracha Chaovalitwongse, 2010. "Optimizing feature selection to improve medical diagnosis," Annals of Operations Research, Springer, vol. 174(1), pages 169-183, February.
    11. Riccardo Bisori & Matteo Lapucci & Marco Sciandrone, 2022. "A study on sequential minimal optimization methods for standard quadratic problems," 4OR, Springer, vol. 20(4), pages 685-712, December.
    12. Tommaso Colombo & Simone Sagratella, 2020. "Distributed algorithms for convex problems with linear coupling constraints," Journal of Global Optimization, Springer, vol. 77(1), pages 53-73, May.
    13. Tommaso Colombo & Massimiliano Mangone & Andrea Bernetti & Marco Paoloni & Valter Santilli & Laura Palagi, 2019. "Supervised and unsupervised learning to classify scoliosis and healthy subjects based on non-invasive rasterstereography analysis," DIAG Technical Reports 2019-08, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

    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:spr:annopr:v:312:y:2022:i:2:d:10.1007_s10479-020-03921-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.