 This paper proposes a novel SNN model called SGLNet which uses Spike, based adaptive graph convolution and LSTM to classify electroencephalogram, e.g., signals. It outperformed existing state of the art e.g. classification algorithms by achieving higher accuracy rates. This article was authored by Kailiang Gong, Peng Pai Wang, Yuying Zhou, and others.