Liu Xiaolong, Yang Banghua, Gan An'an, Zhang Jie
School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai 200444, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Jun 25;42(3):473-479. doi: 10.7507/1001-5515.202503048.
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.
言语想象是一种新兴的脑机接口（BCI）范式，有潜力为言语障碍患者提供有效的交流方式。本研究设计了一种中文言语想象范式，使用三个临床相关词汇——“帮帮我”、“坐起来”和“翻身”——并从15名健康受试者中收集脑电图（EEG）数据。基于这些数据，提出了一种通道注意力多尺度卷积神经网络（CAM-Net）解码算法，该算法将多尺度时间卷积与非对称空间卷积相结合以提取多维EEG特征，并结合通道注意力机制和双向长短期记忆网络来进行通道加权和捕捉时间依赖性。实验结果表明，CAM-Net在三类任务中实现了48.54%的分类准确率，优于EEGNet和深度卷积网络等基线模型，并且在“坐起来”和“翻身”的二分类中达到了64.17%的最高准确率。这项工作为未来中文言语想象BCI的研究和应用提供了一种有前景的方法。
