NiSNN-A: Non-iterative Spiking Neural Networks with Attention with
  Application to Motor Imagery EEG Classification
Chuhan Zhang Wei Pan Cosimo Della Santina
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.