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

Document Type : Original Article


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

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


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.


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