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Exploiting Approximate Joint Diagonalization for Covariance Estimation in Imagined Speech Decoding

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Brain Informatics (BI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13974))

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Abstract

Recently, imagined speech has become a subject of study due to its potential as an intuitive communication system. It involves registering neural responses generated by mental speaking without moving the articulators. Although it may not perform as well as other paradigms, it has multiclass scalability, making it suitable for building extensible BCI systems. Hence, our study revolves around this intuitive paradigm that decodes human speech imagery from EEG signals using Riemannian geometry and a recently introduced covariance estimation method that is based on the concept of Approximate Joint Diagonalization (AJD). The employed methodological framework approach sets its grounds on neuroscientifically sound theories and is being validated on a competition dataset consisting of multichannel EEG trials from five different imagined prompts. Despite its simplicity, the presented methodology achieves over 70% accuracy in some classes, which is on par with State-of-the-Art performance on the dataset. Our methodology performs significantly better in monosyllabic prompts (i.e., ‘yes’ and ‘stop’) which may constitute it more appropriate in immediate-response critical BCI applications. Moreover, the conducted preliminary analysis that was used for sensor selection and onset detection sheds light into the understudied neural phenomena of imagined speech as captured in EEG signals.

*This work was supported by the NeuroMkt project, co-financed by the European Regional Development Fund of the EU and Greek National Funds through the Operational Program “Competitiveness, Entrepreneurship and Innovation”, under RESEARCH CREATE INNOVATE (T2EDK-03661).

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References

  1. Abdulkader, S.N., Atia, A., Mostafa, M.S.M.: Brain computer interfacing: applications and challenges. Egypt. Informa. J. 16(2), 213–230 (2015)

    Article  Google Scholar 

  2. Bakhshali, M.A., Khademi, M., Ebrahimi-Moghadam, A., Moghimi, S.: EEG signal classification of imagined speech based on Riemannian distance of correntropy spectral density. Biomed. Signal Process. Control 59, 101899 (2020)

    Article  Google Scholar 

  3. Cardoso, J.F., Souloumiac, A.: Jacobi angles for simultaneous diagonalization. SIAM J. Matrix Anal. Appl. 17(1), 161–164 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  4. Chengaiyan, S., Retnapandian, A.S., Anandan, K.: Identification of vowels in consonant-vowel-consonant words from speech imagery based EEG signals. Cogn. Neurodyn. 14(1), 1–19 (2020)

    Article  Google Scholar 

  5. Chu, Y., et al.: Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression. J. Neural Eng. 17(4), 046029 (2020)

    Article  Google Scholar 

  6. Congedo, M., Barachant, A., Bhatia, R.: Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Comput. Interfaces 4(3), 155–174 (2017)

    Article  Google Scholar 

  7. Cooney, C., Korik, A., Folli, R., Coyle, D.: Evaluation of hyperparameter optimization in machine and deep learning methods for decoding imagined speech EEG. Sensors 20(16), 4629 (2020)

    Article  Google Scholar 

  8. García-Salinas, J.S., Villaseñor-Pineda, L., Reyes-García, C.A., Torres-García, A.A.: Transfer learning in imagined speech EEG-based BCIs. Biomed. Signal Process. Control 50, 151–157 (2019)

    Article  Google Scholar 

  9. Georgiadis, K., Laskaris, N., Nikolopoulos, S., Kompatsiaris, I.: Connectivity steered graph Fourier transform for motor imagery BCI decoding. J. Neural Eng. 16(5), 056021 (2019)

    Article  Google Scholar 

  10. Georgiadis, K., Adamos, D.A., Nikolopoulos, S., Laskaris, N., Kompatsiaris, I.: A graph-theoretic sensor-selection scheme for covariance-based motor imagery (mi) decoding. In: 2020 28th European Signal Processing Conference (EUSIPCO), pp. 1234–1238. IEEE (2021)

    Google Scholar 

  11. Georgiadis, K., Kalaganis, F.P., Oikonomou, V.P., Nikolopoulos, S., Laskaris, N.A., Kompatsiaris, I.: \(^R\)NeuMark: a Riemannian EEG analysis framework for neuromarketing. Brain Inform. 9(1), 22 (2022)

    Article  Google Scholar 

  12. Herff, C., de Pesters, A., Heger, D., Brunner, P., Schalk, G., Schultz, T.: Towards continuous speech recognition for BCI. Brain-Comput. Interface Res. State Art Summary 5, 21–29 (2017)

    Article  Google Scholar 

  13. Hyvärinen, A.: Survey on independent component analysis (1999)

    Google Scholar 

  14. Jeong, J.H., et al.: 2020 international brain-computer interface competition: a review. Front. Hum. Neurosci. 16, 898300 (2022)

    Article  Google Scholar 

  15. Kalaganis, F.P., Laskaris, N.A., Chatzilari, E., Nikolopoulos, S., Kompatsiaris, I.: A Riemannian geometry approach to reduced and discriminative covariance estimation in brain computer interfaces. IEEE Trans. Biomed. Eng. 67(1), 245–255 (2019)

    Article  Google Scholar 

  16. Kalaganis, F.P., Laskaris, N.A., Oikonomou, V.P., Nikopolopoulos, S., Kompatsiaris, I.: Revisiting Riemannian geometry-based EEG decoding through approximate joint diagonalization. J. Neural Eng. 19(6), 066030 (2022)

    Article  Google Scholar 

  17. Lee, S.H., Lee, M., Jeong, J.H., Lee, S.W.: Towards an EEG-based intuitive BCI communication system using imagined speech and visual imagery. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 4409–4414. IEEE (2019)

    Google Scholar 

  18. Lee, Y.E., Lee, S.H.: EEG-transformer: self-attention from transformer architecture for decoding EEG of imagined speech. In: 2022 10th International Winter Conference on Brain-Computer Interface (BCI), pp. 1–4. IEEE (2022)

    Google Scholar 

  19. Li, F., Xia, Y., Wang, F., Zhang, D., Li, X., He, F.: Transfer learning algorithm of P300-EEG signal based on XDAWN spatial filter and Riemannian geometry classifier. Appl. Sci. 10(5), 1804 (2020)

    Article  Google Scholar 

  20. Lopez-Bernal, D., Balderas, D., Ponce, P., Molina, A.: A state-of-the-art review of EEG-based imagined speech decoding. Front. Hum. Neurosci. 16, 867281 (2022)

    Article  Google Scholar 

  21. Müller, K.R., Vigario, R., Meinecke, F., Ziehe, A.: Blind source separation techniques for decomposing event-related brain signals. Int. J. Bifurcat. Chaos 14(02), 773–791 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  22. Nguyen, C.H., Karavas, G.K., Artemiadis, P.: Inferring imagined speech using EEG signals: a new approach using Riemannian manifold features. J. Neural Eng. 15(1), 016002 (2017)

    Article  Google Scholar 

  23. Pennec, X., Fillard, P., Ayache, N.: A Riemannian framework for tensor computing. Int. J. Comput. Vision 66(1), 41–66 (2006)

    Article  MATH  Google Scholar 

  24. Proix, T., et al.: Imagined speech can be decoded from low-and cross-frequency intracranial EEG features. Nat. Commun. 13(1), 48 (2022)

    Article  Google Scholar 

  25. Si, X., Li, S., Xiang, S., Yu, J., Ming, D.: Imagined speech increases the hemodynamic response and functional connectivity of the dorsal motor cortex. J. Neural Eng. 18(5), 056048 (2021)

    Article  Google Scholar 

  26. Ziehe, A.: Blind source separation based on joint diagonalization of matrices with applications in biomedical signal processing. Ph.D. thesis, Universität Potsdam (2005)

    Google Scholar 

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Authors and Affiliations

  1. Centre for Research & Technology Hellas, Information Technologies Institute (ITI), Thermi -Thessaloniki, Greece

    Fotis P. Kalaganis, Kostas Georgiadis, Vangelis P. Oikonomou, Spiros Nikolopoulos & Ioannis Kompatsiaris

  2. AIIA-Lab, Informatics Dept, AUTH, NeuroInformatics.Group, Thessaloniki, Greece

    Nikos A. Laskaris

Authors
  1. Fotis P. Kalaganis
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  2. Kostas Georgiadis
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  3. Vangelis P. Oikonomou
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  4. Spiros Nikolopoulos
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  5. Nikos A. Laskaris
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  6. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Fotis P. Kalaganis .

Editor information

Editors and Affiliations

  1. Stevens Institute of Technology, Hoboken, NJ, USA

    Feng Liu

  2. Lehigh University, Bethlehem, PA, USA

    Yu Zhang

  3. Maebashi Institute of Technology, Maebashi, Japan

    Hongzhi Kuai

  4. Boston University, Boston, MA, USA

    Emily P. Stephen

  5. Stevens Institute of Technology, Hoboken, NJ, USA

    Hongjun Wang

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Kalaganis, F.P., Georgiadis, K., Oikonomou, V.P., Nikolopoulos, S., Laskaris, N.A., Kompatsiaris, I. (2023). Exploiting Approximate Joint Diagonalization for Covariance Estimation in Imagined Speech Decoding. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham. https://doi.org/10.1007/978-3-031-43075-6_35

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  • DOI: https://doi.org/10.1007/978-3-031-43075-6_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43074-9

  • Online ISBN: 978-3-031-43075-6

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