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Improving P300 Speller performance by means of optimization and machine learning

Author

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  • 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
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. Veronica Piccialli & Marco Sciandrone, 2018. "Nonlinear optimization and support vector machines," 4OR, Springer, vol. 16(2), pages 111-149, June.
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