Banovoth Raja Sekhar, K V Kadambari
Department of Computer Science and Engineering, National Institute of Technology Warangal, Telangana, 506004, India.
Comput Biol Med. 2025 Aug;194:110397. doi: 10.1016/j.compbiomed.2025.110397. Epub 2025 Jun 10.
The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit rich neurodynamic features in the spatiotemporal domain, making them well-suited for processing brain signals, mainly those captured using the widely used non-invasive Electroencephalography (EEG) technique. However, the structural limitations of SNN hinder their feature extraction capabilities for motor imagery signal classification, which leads to under performance of the task. To address the aforementioned challenge, the proposed study introduces a novel model that incorporates Roman Domination within a Spiking Neural Network (RDSNN), where Roman domination identifies the most highly correlated channels or nodes. These channels generate an appropriate threshold for spike generation in the signals, which are then classified using the SNN. The model's performance was evaluated on three typically representative motor imagery datasets: PhysioNet, BCI Competition IV-2a, and BCI Competition IV-2b. RDSNN achieved 73.65% accuracy on PhysioNet, 81.75% on BCI IV-2a, and 84.56% on BCI IV-2b. The results demonstrate not only superior accuracy compared to State-Of-the-Art (SOTA) methods but also a 35% reduction in computation time, attributed to the application of Roman domination.
脉冲神经网络（SNN）是第三代神经网络，以其能源效率和处理时空信息的能力而闻名，它紧密模仿大脑中生物神经元的行为机制。SNN在时空域中展现出丰富的神经动力学特征，使其非常适合处理脑信号，主要是那些使用广泛应用的非侵入性脑电图（EEG）技术捕获的信号。然而，SNN的结构局限性阻碍了它们对运动想象信号分类的特征提取能力，这导致任务表现不佳。为了应对上述挑战，本研究提出了一种新颖的模型，即在脉冲神经网络（RDSNN）中纳入罗马支配，其中罗马支配可识别相关性最高的通道或节点。这些通道为信号中的脉冲生成确定一个合适的阈值，然后使用SNN进行分类。该模型在三个具有典型代表性的运动想象数据集上进行了性能评估：PhysioNet、BCI竞赛IV-2a和BCI竞赛IV-2b。RDSNN在PhysioNet上的准确率达到73.65%，在BCI IV-2a上为81.75%，在BCI IV-2b上为84.56%。结果表明，与现有最先进（SOTA）方法相比，该模型不仅具有更高的准确率，而且由于应用了罗马支配，计算时间减少了35%。
