Decoding Speech Imagination for Words of Rock-Paper-Scissors Game using EEG Signals

Document Type : Original Article

Authors

1 Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran

2 Department of Electronic and Computer Engineering, Brunel University, London, UK

Abstract

Recognition of silent speech only by decoding brain signals is one of the latest research in the field of artificial intelligence. The 3rd Iranian national brain-computer interface competition, which was held by the National Brain Mapping Center of Iran in 2020, was dedicated to the classification of imagined speech for the three words of rock-paper-scissors game. In this contest, the authors introduced an approach based on wavelet packets decomposition and common spatial pattern and could win the second place. We evaluated some of the most famous classifiers including support vector machine, k-nearest neighbor, random forest, logistic regression, XGBoost and dense deep learning model separately for each subject and simultaneously for all subjects. The best average accuracy was 51.7%. Then we developed the model using spectrogram and convolutional neural network and achieved average accuracy of 76.5%. The accuracy was much better than the accuracy reported by other researchers on this dataset. Also, the performance of our model is superior to recent research in this field on different datasets.

Keywords

References

[1] P.R. Roelfsema, D. Denys, P.C. Klink, Mind reading and writing: The future of neurotechnology, Trends in cognitive sciences 22 (2018)
[2] D. Dash, P. Ferrari, J. Wang, Decoding imagined and spoken phrases from non-invasive neural (MEG) signals, Frontiers in neuroscience 14 (2020).
[3] Y. Wang, M. Zhang, R. Wu, H. Gao, M. Yang, Z. Luo, G. Li, Silent speech decoding using spectrogram features based on neuromuscular activities, Brain Sciences 10 (2020) 442.
[4] A.R. Sereshkeh, R. Yousefi, A.T. Wong, T. Chau, Online classification of imagined speech using functional near-infrared spectroscopy signals, Journal of neural engineering 16 (2018) 016005.
[5] S. Martin, I. Iturrate, P. Brunner, J.d.R. Millán, G. Schalk, R.T. Knight, B.N. Pasley, Individual Word Classification During Imagined Speech Using Intracranial Recordings,  Brain-Computer Interface Research, Springer2019, pp. 83-91.
[6] A. Aleman, E. Formisano, H. Koppenhagen, P. Hagoort, E.H. De Haan, R.S. Kahn, The functional neuroanatomy of metrical stress evaluation of perceived and imagined spoken words, Cerebral Cortex 15 (2005) 221-228.
[7] L. Wang, X. Zhang, X. Zhong, Y. Zhang, Analysis and classification of speech imagery EEG for BCI, Biomedical signal processing and control 8 (2013) 901-908.
[8] T. Hernández-Del-Toro, C.A. Reyes-García, L. Villaseñor-Pineda, Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals, Biomedical Signal Processing and Control 65 (2021) 102351.
[9] C. Cooney, A. Korik, R. Folli, D. Coyle, Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG, Sensors 20 (2020) 4629.
[10] M. Jiménez-Guarneros, P. Gómez-Gil, Standardization-refinement domain adaptation method for cross-subject EEG-based classification in imagined speech recognition, Pattern Recognition Letters 141 (2021).
[11] N. Hamedi, S. Samiei, M. Delrobaei, A. Khadem, Imagined Speech Decoding From EEG: The Winner of 3rd Iranian BCI Competition (iBCIC2020),  2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), IEEE, 2020.
[12] https://nbml.ir/FA/scientific-tournament/102640/ (accessed 13 October 2020.
[13] Makoto's preprocessing pipeline, https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline (accessed 25 September 2019.
[14] T. Fushiki, Estimation of prediction error by using K-fold cross-validation, Statistics and Computing 21 (2011) 137-146.
[15] H. Ramoser, J. Muller-Gerking, G. Pfurtscheller, Optimal spatial filtering of single trial EEG during imagined hand movement, IEEE transactions on rehabilitation engineering 8 (2000) 441-446.
[16] M. Moghaddari, M.Z. Lighvan, S. Danishvar, Diagnose ADHD disorder in children using convolutional neural network based on continuous mental task EEG, Computer Methods and Programs in Biomedicine 197 (2020).
Advanced Signal Processing
Volume 4, Issue 2 - Serial Number 6
December 2020
Pages 279-289

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