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This paper proposes SaleNet - an end-to-end convolutional neural network(CNN) for sustained attention level evaluation using prefrontalelectroencephalogram (EEG). A bias-driven pruning method is proposed togetherwith group convolution, global average pooling (GAP), near-zero pruning, weightclustering and quantization for the model compression, achieving a totalcompression ratio of 183.11x. The compressed SaleNet obtains a state-of-the-artsubject-independent sustained attention level classification accuracy of 84.2%on the recorded 6-subject EEG database in this work. The SaleNet is implementedon a Artix-7 FPGA with a competitive power consumption of 0.11 W and anenergy-efficiency of 8.19 GOps/W.

SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG