This study presents a comprehensive exploration of EEG-based motor imagery classification using advanced deep learning architectures. Focusing on six distinct motor imagery classes, we investigate the performance of convolutional neural networks (CNN), CNN with Long Short-Term Memory (CNN-LSTM), and CNN with Bidirectional LSTM (CNN-BILSTM) models. The CNN architecture excels with a remarkable accuracy of 99.86%, while the CNN-LSTM and CNN-BILSTM models achieve 98.39% and 99.27%, respectively, showcasing their effectiveness in decoding EEG signals associated with imagined movements.The results underscore the potential applications of this research in fields such as assistive robotics and automation, showcasing the ability to translate cognitive intent into robotic actions. This study offers valuable insights into the realm of deep learning for EEG analysis, setting the stage for advancements in brain-computer interfaces and human-machine interaction.
Deep Learning for EEG-Based Motor Imagery Classification: Towards Enhanced Human-Machine Interaction and Assistive Robotics / Boutarfaia, N.; Russo, S.; Tibermacine, A.; Tibermacine, I. E.. - 3695:(2023), pp. 68-74. (Intervento presentato al convegno 9th Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2023 tenutosi a Rome; Italy).
Deep Learning for EEG-Based Motor Imagery Classification: Towards Enhanced Human-Machine Interaction and Assistive Robotics
Russo S.;Tibermacine I. E.
2023
Abstract
This study presents a comprehensive exploration of EEG-based motor imagery classification using advanced deep learning architectures. Focusing on six distinct motor imagery classes, we investigate the performance of convolutional neural networks (CNN), CNN with Long Short-Term Memory (CNN-LSTM), and CNN with Bidirectional LSTM (CNN-BILSTM) models. The CNN architecture excels with a remarkable accuracy of 99.86%, while the CNN-LSTM and CNN-BILSTM models achieve 98.39% and 99.27%, respectively, showcasing their effectiveness in decoding EEG signals associated with imagined movements.The results underscore the potential applications of this research in fields such as assistive robotics and automation, showcasing the ability to translate cognitive intent into robotic actions. This study offers valuable insights into the realm of deep learning for EEG analysis, setting the stage for advancements in brain-computer interfaces and human-machine interaction.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.