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Adaptive compressed sensing architecture in wireless brain-computer interface

Aosen Wang, Zhanpeng Jin, Chen Song, Wenyao Xu
2015 Proceedings of the 52nd Annual Design Automation Conference on - DAC '15  
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 signal
more » ... of EEG signal acquisition. Specifically, we design a dynamic knob framework to respond to EEG signal dynamics, and then formulate its design optimization into a dynamic programming problem. We verify our proposed adaptive CS architecture on a publicly available data set. Experimental results show that our adaptive CS can improve signal reconstruction quality by more than 70% under different energy budgets while only consuming 187.88 nJ/event. This indicates that the adaptive CS architecture can effectively adapt to the EEG signal dynamics in the BCI.
doi:10.1145/2744769.2744792 dblp:conf/dac/WangJSX15 fatcat:cif2ivih2ra5jpz6pshjxpnhtu