Classification of EEG Signal Using Deep Learning Architectures Based Motor-Imagery for an Upper-Limb Rehabilitation Exoskeleton

• Published in: SN computer science Vol. 6; no. 3; p. 193
• Main Authors: Titkanlou, Maryam Khoshkhooy, Pham, Duc Thien, Mouček, Roman
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 18.02.2025; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Classification; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data collection; Data Structures and Information Theory; Datasets; Deep learning; Electrodes; Electroencephalography; Exoskeletons; Human-computer interface; Imagery; Information Systems and Communication Service; Machine learning; Neural networks; Original Research; Pattern Recognition and Graphics; Rehabilitation; Research and Development on Biomedical Engineering Systems and Technologies (BIOSTEC 2024); Robots; Software Engineering/Programming and Operating Systems; Vision; Deep neural network; Electroencephalography; Motor imagery; ERD/ERS; Brain–computer interface
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-025-03743-6

The brain-computer interface (BCI) is an emerging technology that enables people with physical disabilities to control and interact with devices only by using their minds and without being dependent on healthy people. One of the most popular BCI paradigms, motor imagery (MI) based on electroencephalograms (EEGs), is applied in healthcare, including rehabilitation. A significant challenge in classifying EEG signals using deep learning methods is the accurate recognition of MI signals. CNN-LSTM and CNN-Transformer are two classification algorithms proposed to improve the classification accuracy of Motor Imagery EEG signals in a noninvasive brain-computer interface. Three different methods, including noise injection (NI), conditional variational autoencoder (cVAE), and conditional GAN with Wasserstein price function and gradient penalty (cWGAN-GP), have also been implemented to augment this dataset. The best accuracy was achieved by the CNN-LSTM model, which is 79.06%, using an MI dataset involving hand movements. The dataset included 29 healthy subjects, with males aged 21–26 and females aged 18–23.