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Motor Imagery Classification Combining Riemannian Geometry and Artificial Neural Networks

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1704))

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Abstract

Brain-computer interfaces (BCIs) based on non-invasive electroencephalography provide a means of communication for people with severe disabilities. BCI based on the detection of motor imagery can be used for both communication and rehabilitation purposes [8]. For transferring BCIs outside of the lab to clinical settings, it is necessary to have a high accuracy. The current state of the art techniques includes the use of distance based on the Riemannian geometry. In this paper, we propose a new pattern recognition system for the multiclass classification of brain evoked responses corresponding to motor imagery. The method is based on the combination of features based on Riemannian geometry obtained from 15 frequency bands from 8 24 Hz to cover the mu and beta bands, and a feedforward neural network for the classification. We compare the performance of the multi-layer perceptron (MLP) and the extreme learning machine (ELM) classifiers. The system has been assessed on two publicly available datasets. The kappa value for 4-class is 0.53. The average binary classification across the six pairwise tasks is 80.83%. The results support the conclusion that multi-band classification can be successfully achieved using artificial neural networks and MLPs provide substantially better performance than ELMs approaches.

This study was supported by the NIH-R15 NS118581 project.

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Authors and Affiliations

  1. Department of Computer Science, California State University, Fresno, CA, USA

    Hubert Cecotti & Girish Tiwale

Authors
  1. Hubert Cecotti
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  2. Girish Tiwale
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Corresponding author

Correspondence to Hubert Cecotti .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Texas A&M University, College Station, TX, USA

    Ayush Goyal

  3. University of Luxembourg, Luxembourg, Luxembourg

    Djamila Aouada

  4. University of Derby, Derby, UK

    Aaisha Makkar

  5. University of Minnesota, Minneapolis, MN, USA

    Yao-Yi Chiang

  6. IIIT Allahabad, Allahabad, India

    Satish K Singh

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Cecotti, H., Tiwale, G. (2023). Motor Imagery Classification Combining Riemannian Geometry and Artificial Neural Networks. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_13

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  • DOI: https://doi.org/10.1007/978-3-031-23599-3_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23598-6

  • Online ISBN: 978-3-031-23599-3

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