10.25673/122075">


Proceedings of International Conference on Applied Innovation in IT
2025/08/29, Volume 13, Issue 4, pp.117-123

A Deep Learning-Driven Filter Bank CSP Approach for Motor Imagery EEG Decoding


Reem Dheyaa Ismael, Rasha Abdalla Yousif, Wisal Saud Hussein, Rasha Ahmed Jelab, Mohamed Aktham Ahmed and Zaidoon Tareq Abdulwahhab


Abstract: Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs) have garnered considerable interest for facilitating direct neurological control of external devices, especially in assistive and rehabilitative technologies. However, the proficient classification of non-stationary, low-amplitude EEG signals continue to remain a significant difficulty. This study introduces a hybrid framework that combines Filter Bank Common Spatial Pattern (FBCSP) with a 1D Convolutional Neural Network (CNN) to improve the classification of motor imagery signals. Electroencephalogram data were acquired from four participants utilizing a 14-channel Emotive EPOC headset during two-class motor imaging tasks (left versus right hand imagery). The EEG samples were bandpass filtered into three frequency sub-bands (8-12 Hz, 12-16 Hz, and 16-30 Hz), and to extract discriminative spatial features the CSP was applied to each band. These features were combined and normalized before being entered into a lightweight CNN model for classification. The model was trained with the Adam optimizer and evaluated using standard metrics. Subject-specific results showed high classification ability, with accuracy approaching 100% for some individuals and an average accuracy above 90% across most subjects. The proposed FBCSP + CNN pipeline effectively captures spatial-spectral patterns in EEG data while being computationally inexpensive, making it ideal for real-time BCI applications that use consumer-grade EEG sensors. These findings emphasize the utility of hybrid handcrafted-deep learning models in actual MI-BCI systems.

Keywords: Brain-Computer Interface, Motor Imagery, EEG, Filter Bank CSP, Convolutional Neural Network, Emotiv EPOC.

DOI: 10.25673/122075

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