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