A Comparative Study of Sequential and Attention Architectures for Cross-Subject EEG Motor Imagery Classification

Authors

  • Krishna Jaswitha Kellampalli, Panamala Prasana Kumari,V.Krishna Pratap,K.Srinivasa Rao Author

DOI:

https://doi.org/10.48047/n8n2ak67

Keywords:

Brain Tumour Detection, Magnetic Resonance Imaging (MRI), Medical Image Analysis, Machine Learning (ML), Deep Learning (DL), Convolutional Neural Network (CNN), Explainable Artificial Intelligence (XAI), Transfer Learning, VGG16, Tumour Classification, Saliency Maps, Grad CAM, Diagnostic Accuracy, Automated Healthcare Systems, Clinical Decision Support.

Abstract

Brain tumours are among the most critical neurological disorders and require early and accurate diagnosis to improve patient survival rates and treatment planning. Magnetic Resonance Imaging (MRI) is widely  used for brain tumour diagnosis because of its superior soft tissue contrast and non-invasive imaging capability

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Published

23.05.2026

Issue

Vol. 16 No. 2 (2026): volume 16 issue 2(2026)

Section

Articles

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How to Cite

A Comparative Study of Sequential and Attention Architectures for Cross-Subject EEG Motor Imagery Classification. (2026). International Journal of Information and Electronics Engineering, 16(2), 394-400. https://doi.org/10.48047/n8n2ak67