IHCL-Net: an improved hybrid CNN and LSTM network for feature extraction and classification of motor imagery EEG signals

Hang Zhang; Hui Peng; Zhenzhou Feng; Yanchao Lou; Juan Yang

doi:10.1117/12.3033733

13 June 2024 IHCL-Net: an improved hybrid CNN and LSTM network for feature extraction and classification of motor imagery EEG signals

Hang Zhang, Hui Peng, Zhenzhou Feng, Yanchao Lou, Juan Yang

Author Affiliations +

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131801T (2024) https://doi.org/10.1117/12.3033733
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Aiming at the problem of poor classification performance caused by the loss of useful information in the process of feature extraction of moving image brain computer interface (MI-BCI). This study describes IHCL-Net, an improved hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network. We show that IHCL-Net effectively reduces feature loss during model training and accurately extracts spatiotemporal features from EEG data. Our proposed network accurately maps the temporal correlation of signals to a new space during the extraction of spatial features, which preserves the integrity of temporal characteristics as much as possible. Preliminary results showed that the IHCL-Net had the average accuracy value of 85.3% on IV-2a compared with other networks, achieving the highest classification performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hang Zhang, Hui Peng, Zhenzhou Feng, Yanchao Lou, and Juan Yang "IHCL-Net: an improved hybrid CNN and LSTM network for feature extraction and classification of motor imagery EEG signals", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131801T (13 June 2024); https://doi.org/10.1117/12.3033733

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available