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Motor imagery, an important category in electroencephalogram (EEG) research,often intersects with scenarios demanding low energy consumption, such asportable medical devices and isolated environment operations. Traditional deeplearning algorithms, despite their effectiveness, are characterized bysignificant computational demands accompanied by high energy usage. As analternative, spiking neural networks (SNNs), inspired by the biologicalfunctions of the brain, emerge as a promising energy-efficient solution.However, SNNs typically exhibit lower accuracy than their counterpartconvolutional neural networks (CNNs). Although attention mechanismssuccessfully increase network accuracy by focusing on relevant features, theirintegration in the SNN framework remains an open question. In this work, wecombine the SNN and the attention mechanisms for the EEG classification, aimingto improve precision and reduce energy consumption. To this end, we firstpropose a Non-iterative Leaky Integrate-and-Fire (LIF) neuron model, overcomingthe gradient issues in the traditional SNNs using the Iterative LIF neurons.Then, we introduce the sequence-based attention mechanisms to refine thefeature map. We evaluated the proposed Non-iterative SNN with Attention(NiSNN-A) model on OpenBMI, a large-scale motor imagery dataset. Experimentresults demonstrate that 1) our model outperforms other SNN models by achievinghigher accuracy, 2) our model increases energy efficiency compared to thecounterpart CNN models (i.e., by 2.27 times) while maintaining comparableaccuracy.

NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification