Hamdi Altaheri - A Multi-Branch Convolutional Neural Network with

Journal article

G. Altuwaijri, G. Muhammad, Hamdi Altaheri, Mansour Alsulaiman
Diagnostics, 2022

Semantic Scholar DOI PubMedCentral PubMed

Cite

APA Click to copy
Altuwaijri, G., Muhammad, G., Altaheri, H., & Alsulaiman, M. (2022). A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification. Diagnostics.

Chicago/Turabian Click to copy
Altuwaijri, G., G. Muhammad, Hamdi Altaheri, and Mansour Alsulaiman. “A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification.” Diagnostics (2022).

MLA Click to copy
Altuwaijri, G., et al. “A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification.” Diagnostics, 2022.

BibTeX Click to copy

@article{g2022a,
  title = {A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification},
  year = {2022},
  journal = {Diagnostics},
  author = {Altuwaijri, G. and Muhammad, G. and Altaheri, Hamdi and Alsulaiman, Mansour}
}

Abstract

Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate with the outside world via assistive technology. Regrettably, EEG decoding is challenging because of the complexity, dynamic nature, and low signal-to-noise ratio of the EEG signal. Developing an end-to-end architecture capable of correctly extracting EEG data’s high-level features remains a difficulty. This study introduces a new model for decoding MI known as a Multi-Branch EEGNet with squeeze-and-excitation blocks (MBEEGSE). By clearly specifying channel interdependencies, a multi-branch CNN model with attention blocks is employed to adaptively change channel-wise feature responses. When compared to existing state-of-the-art EEG motor imagery classification models, the suggested model achieves good accuracy (82.87%) with reduced parameters in the BCI-IV2a motor imagery dataset and (96.15%) in the high gamma dataset.