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The findings indicate that EEGNet outperformed the traditional method in three out of the four classification scenarios, achieving average accuracy values of 90.69 ± 5.21% for Attention vs. Pronunciation, 73.91 ± 10.04% for Short words vs. Long words, 81.23 ± 10.47% for Word vs.
Nov 4, 2023 · EEGNet outperformed the PSD + SVM method in three of the four classification scenarios. These findings demonstrate the potential of EEGNet for ...
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This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary.
Using a 2D Convolutional Neural Network (CNN) based on the EEGNet architecture, we classified the EEG signals from eight subjects when they internally thought ...
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In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning.
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Jan 19, 2022 · This study focuses on providing a simple, extensible, and multiclass classifier for imagined words using EEG signals.
Nov 18, 2023 · Denise Alonso-Vázquez's Post · EEG-Based Classification of Spoken Words Using Machine Learning Approaches · More Relevant Posts · Explore topics.
Nov 20, 2021 · An artificial neural networks (ANN) in combination with PCA have also been used to classify imagined speech from EEG signals [12]. However, most ...
In this paper, we propose a rotation based ensemble approach for covert speech classification using EEG data.
May 27, 2023 · In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted ...
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