Motor imagery, an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations. Traditional deep learning algorithms, despite their effectiveness, are characterized by significant computational demands accompanied by high energy usage. As an alternative, spiking neural networks (SNNs), inspired by the biological functions of the brain, emerge as a promising energy-efficient solution. However, SNNs typically exhibit lower accuracy than their counterpart convolutional neural networks (CNNs). Although attention mechanisms successfully increase network accuracy by focusing on relevant features, their integration in the SNN framework remains an open question. In this work, we combine the SNN and the attention mechanisms for the EEG classification, aiming to improve precision and reduce energy consumption. To this end, we first propose a Non-iterative Leaky Integrate-and-Fire (LIF) neuron model, overcoming the gradient issues in the traditional SNNs using the Iterative LIF neurons. Then, we introduce the sequence-based attention mechanisms to refine the feature map. We evaluated the proposed Non-iterative SNN with Attention (NiSNN-A) model on OpenBMI, a large-scale motor imagery dataset. Experiment results demonstrate that 1) our model outperforms other SNN models by achieving higher accuracy, 2) our model increases energy efficiency compared to the counterpart CNN models (i.e., by 2.27 times) while maintaining comparable accuracy.

翻译：摘要：运动想象是脑电图研究中的重要类别，常与低能耗需求场景（如便携式医疗设备和隔离环境操作）交织。传统深度学习算法虽然有效，但计算需求大且能耗高。作为替代方案，脉冲神经网络受大脑生物功能启发，成为一种有前景的节能解决方案。然而，SNN通常比对应的卷积神经网络精度更低。尽管注意力机制通过关注相关特征成功提升了网络精度，但其在SNN框架中的整合仍是一个开放问题。在本研究中，我们将SNN与注意力机制结合用于脑电图分类，旨在提高精度并降低能耗。为此，我们首先提出非迭代泄露型整合-发放（LIF）神经元模型，克服了传统基于迭代LIF神经元的SNN中的梯度问题。接着，我们引入基于序列的注意力机制以优化特征图。我们在大规模运动想象数据集OpenBMI上评估了所提出的带注意力的非迭代SNN（NiSNN-A）模型。实验结果表明：1）我们的模型通过实现更高精度超越其他SNN模型；2）与对应的CNN模型相比（即能效提升2.27倍），我们的模型在保持可比精度的同时提高了能效。