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Wireless sensor nodes advance the brain-computer interface (BCI) from laboratory setup to practical applications. Compressed sensing (CS) theory provides a sub-Nyquist sampling paradigm to improve the energy efficiency of electroencephalography (EEG) signal acquisition. However, EEG is a structure-variational signal with time-varying sparsity, which decreases the efficiency of compressed sensing. In this paper, we present a new adaptive CS architecture to tackle the challenge of EEG signaldoi:10.1145/2744769.2744792 dblp:conf/dac/WangJSX15 fatcat:cif2ivih2ra5jpz6pshjxpnhtu