Study on speech imagery electroencephalography decoding of Chinese words based on the CAM-Net model.
10.7507/1001-5515.202503048
- Author:
Xiaolong LIU
1
;
Banghua YANG
1
;
An'an GAN
1
;
Jie ZHANG
1
Author Information
1. School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200444, P. R. China.
- Publication Type:Journal Article
- Keywords:
Attention mechanism;
Brain-computer interface;
Long short-term memory network;
Speech imagery
- MeSH:
Humans;
Electroencephalography/methods*;
Brain-Computer Interfaces;
Neural Networks, Computer;
Speech/physiology*;
Algorithms;
Male;
Adult;
Imagination
- From:
Journal of Biomedical Engineering
2025;42(3):473-479
- CountryChina
- Language:Chinese
-
Abstract:
Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant words-"Help me", "Sit up" and "Turn over"-and collected electroencephalography (EEG) data from 15 healthy subjects. Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies. Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between "Sit up" and "Turn over". This work provides a promising approach for future Chinese speech imagery BCI research and applications.
