Abstract：The monitoring of electroencephalogram (EEG) signals imposes stringent demands on the real-time performance and energy efficiency of computing systems, requiring the preservation of high temporal resolution while minimizing resource consumption. Spiking neural networks (SNNs), through their spike-based information transmission mechanism, replace the multiplication operations in traditional neural networks with addition operations. Their asynchronous communication characteristics enhance the processing efficiency of temporal data, thereby meeting real-time requirements. As a result, SNNs exhibit significant application potential in continuous EEG monitoring tasks. However, the direct design and optimization of SNNs are highly complex and challenging to deploy efficiently. To address this issue, this study proposed a quantize-clamp-quantize (QCQ) method for converting convolutional neural networks (CNNs) to SNNs and applied it to the SEED dataset for EEG emotion classification and the BCI Competition IV Dataset 2a for motor imagery. Specifically, the QCQ method introduced clamping and quantization operations in the activation and output layers of computational layers, adopted a single quantization strategy for input and pooling layer computations, and migrated batch normalization parameters to achieve efficient CNN-to-SNN conversion. The proposed QCQ method achieved accuracies of 93.79% on the SEED dataset and 78.64% on the MI dataset, yielding average performance gains of 2.77% and 4.31% over advanced existing methods, respectively. These improvements were accomplished with substantial energy efficiency enhancements of nearly 75% and 80% on the two datasets. These results validated that the QCQ method effectively inherited the classification performance of CNNs, brought SNN performance closer to CNNs on both datasets, and provided a practical implementation pathway for SNN applications.
通讯作者:*顾正晖,华南理工大学自动化科学与工程学院教授,博士生导师。2003年毕业于新加坡南洋理工大学信号处理专业,获博士学位。2002年至2008年在新加坡资讯通信研究所信号处理部门工作,2008年至今在华南理工大学脑机接口与脑信息处理中心开展脑电解码算法与脑机接口系统研究。在IEEE Trans Biomed Eng和IEEE Trans Neural Syst Rehab Eng等生物医学工程领域国际权威期刊上发表多篇论文,授权专利多项。主持或参与搭建了本中心多个脑机接口系统平台;主持国家自然科学基金面上和青年项目共3项,参与国家自然科学基金重点项目和联合基金项目等多个重大项目。E-mail: zhgu@scut.edu.cn
引用本文:
赵泽辰, 余天佑, 俞祝良, 顾正晖. 转换型脉冲神经网络及其在脑机接口中的应用研究[J]. 中国生物医学工程学报, 2026, 45(4): 444-456.
Zhao Zechen, Yu Tianyou, Yu Zhuliang, Gu Zhenghui. Transformational Spiking Neural Network and its Application in Brain-Computer Interface. Chinese Journal of Biomedical Engineering, 2026, 45(4): 444-456.
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