Article
Version 3
Preserved in Portico This version is not peer-reviewed
Imagined Speech Classification Using EEG and Deep Learning
Version 1
: Received: 18 April 2023 / Approved: 19 April 2023 / Online: 19 April 2023 (08:45:48 CEST)
Version 2 : Received: 6 May 2023 / Approved: 8 May 2023 / Online: 8 May 2023 (05:30:02 CEST)
Version 3 : Received: 13 May 2023 / Approved: 15 May 2023 / Online: 15 May 2023 (05:43:54 CEST)
Version 2 : Received: 6 May 2023 / Approved: 8 May 2023 / Online: 8 May 2023 (05:30:02 CEST)
Version 3 : Received: 13 May 2023 / Approved: 15 May 2023 / Online: 15 May 2023 (05:43:54 CEST)
A peer-reviewed article of this Preprint also exists.
Abdulghani, M.M.; Walters, W.L.; Abed, K.H. Imagined Speech Classification Using EEG and Deep Learning. Bioengineering 2023, 10, 649, doi:10.3390/bioengineering10060649. Abdulghani, M.M.; Walters, W.L.; Abed, K.H. Imagined Speech Classification Using EEG and Deep Learning. Bioengineering 2023, 10, 649, doi:10.3390/bioengineering10060649.
Abstract
In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel Electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer number of sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: Up, Down, Left, and Right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration has been implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based Brain-Computer Interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50% and 92.62% for precision, recall, and F1-score, respectively.
Keywords
Inner Speech; Imagined Speech; EEG Decoding; Brain-Computer Interface; BCI; LSTM; Wavelet Scattering Transformation; WST.
Subject
Engineering, Bioengineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Commenter: Khalid Abed
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