Development of a CNN-LSTM Deep Learning Model for Motor Imagery EEG Classification for BCI Applications Academic Article uri icon

abstract

  • Brain-Computer Interface (BCI) systems offer a groundbreaking method for the human brain to directly communicate with external devices, serving applications, such as assistive technology, smart environments, and healthcare. Motor Imagery (MI) brain signals derived from Electroencephalography (EEG) are commonly utilized in various BCI fields. However, accurately classifying MI-based EEG signals remains a significant challenge, with traditional classification techniques struggling to effectively capture both spatial and temporal features, resulting in suboptimal performance. Therefore, this study introduces a novel hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) framework designed for EEG-MI task classification. The model combines adaptive learning with optimal training to significantly improve classification performance using the Berlin BCI Dataset 1 from BCI Competition IV. The proposed CNN-LSTM model achieves a classification accuracy of 98.38% on subject independence evaluation. This research compares subject-dependent and subject-independent evaluation with traditional Machine Learning (ML) methods, such as Support Vector Machines (SVM), Random Forest (RF), and Linear Discriminant Analysis (LDA), as well as Deep Learning (DL) models, such as EEGNet, K-nearest Neighbor (KNN), and Convolutional Neural Network (CNN). Extensive evaluations and cross-validation prove the model's superior performance, thus this work sets a benchmark for real-time MI-EEG classification, offering a scalable solution for practical BCI applications.

publication date

  • 2025

number of pages

  • 6

start page

  • 22705

end page

  • 22711

volume

  • 15

issue

  • 3