Online adaptive data acquisition enabling ultra-low power real-world EEG

A continuing challenge to measuring EEG outside of laboratory environments is high noise and low SNR, which traditionally is overcome using low-noise, high-resolution DAQ components. These devices have a high power consumption and require frequent charging, making them impractical for fieldable EEG technology. Here, we discuss an alternative approach that achieves high-resolution signal acquisition with ultra-low power consumption by modulating components of the analog front-end on a sample-by-sample basis. In this work we show that an adaptive data acquisition system can sample neural activity with sufficient resolution for a common BCI task with an average power consumption that is low enough to make it feasible to power the system entirely using energy harvested from the environment.