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Optimizing ML Algorithms Under CSP and Riemannian Covariance in MI-BCIs

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HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13519))

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Abstract

Motor imagery brain-computer interface (MI-BCI) systems face a multitude of challenges, one of which is optimizing multiclass classification of electroencephalography (EEG) signals. Hersche et al. (2018) extracted features from the 4-class BCI competition IV-2a data using Common Spatial Patterns (CSP) and Riemannian Covariance methods which resulted in improved performance speed and accuracy when fed to Support Vector Machines (SVM). We propose testing a variety of classifiers for both feature extraction methods to see their relative performance compared to SVM and to observe the impact of the two different feature extraction methods aforementioned on the different classifiers. SVM performed best, and ensemble algorithms had poor performance- especially AdaBoost. CSP feature extraction resulted in improved accuracy for most algorithms, but consumed more time, whereas the Riemannian feature extraction was twice-faster runtime for all algorithms, as expected. These results provide better understanding of feature extraction using CSP or Riemannian Covariance for MI-BCI data.

Y. Windhorse and N. Almadbooh—Contributed equally to the paper.

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Authors and Affiliations

  1. Swarthmore College, Swarthmore, PA, 19081, USA

    Yang Windhorse & Nader Almadbooh

Authors
  1. Yang Windhorse
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  2. Nader Almadbooh
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Corresponding author

Correspondence to Nader Almadbooh .

Editor information

Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  2. Tokyo University of Science, Tokyo, Saitama, Japan

    Sakae Yamamoto

  3. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  4. Soar Technology Inc., Orlando, FL, USA

    Dylan D. Schmorrow

  5. Katmai Government Services, Orlando, FL, USA

    Cali M. Fidopiastis

  6. Smart Future Initiative, Frankfurt am Main, Germany

    Norbert A. Streitz

  7. Kyushu University, Fukuoka, Japan

    Shin'ichi Konomi

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Windhorse, Y., Almadbooh, N. (2022). Optimizing ML Algorithms Under CSP and Riemannian Covariance in MI-BCIs. In: Kurosu, M., et al. HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments. HCII 2022. Lecture Notes in Computer Science, vol 13519. Springer, Cham. https://doi.org/10.1007/978-3-031-17618-0_38

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  • DOI: https://doi.org/10.1007/978-3-031-17618-0_38

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  • Online ISBN: 978-3-031-17618-0

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