Development of Low-power and High-accuracy Wireless EEG Transmission System Using Compressed Sensing with an EEG Basis

Achieving power savings while maintaining accuracy is essential for wireless electroencephalogram (EEG) measurement devices, enabling them to be lighter with smaller batteries and longer operating times. To meet this requirement, we developed a wireless EEG transmission system that utilizes compressed sensing (CS) with random undersampling to achieve high-accuracy reconstruction while reducing sensing and transmission power. As a key feature of the implemented system, we designed and employed a suitable basis from previously obtained EEG signals for the block sparse Bayesian learning algorithm. Measurements showed that our system achieved significant power savings with a compression ratio of 6, consuming only 72 µW, which is lower than that reported in the latest CS-based study. Notably, despite the reduced power consumption, we reduced the normalized mean square error to 0.116, achieving more than twice the reconstruction accuracy reported in the previous study.