International Symposium on Telecommunication (IST)

Home / 11th International Symposium on Telecommunication (IST'2024)

An Efficient Motor Imagery BCI Classification Architecture Using Non-linear Network Tuning suitable for Cloud-Edge Environments

Authors :

Abolfazl Danayi¹ Hamid Soltanian-Zadeh²

1- School of Electrical and Computer Engineering University of Tehran, Iran 2- School of Electrical and Computer Engineering University of Tehran, Iran

Keywords :

Brain Computer Interface (BCI)،EEG،Deep Learning،Convolutional Neural Network (CNN)،Non-linear Network Tuning Block (NTB)،Edge Computing،Real-World Implementation،Two-Phase Training

Abstract :

This paper proposes a novel deep learning architecture for EEG-based Brain-Computer Interface (BCI) systems, optimized for real-world deployment. BCI is fundamentally a communication problem, yet many works focus on lab-based implementations. Our approach addresses practical implementation considerations crucial for use beyond controlled environments. By incorporating a non-linear Network Tuning Block (NTB) and applying a two-phase training process, our model enhances classification accuracy while minimizing computational demands. Tested on the BCI Competition IV-2b dataset, our architecture achieves a 93.2% accuracy, surpassing state-of-the-art methods with fewer parameters, underscoring its potential for practical, real-world applications.