Using novel MEMS EEG sensors in detecting drowsiness application

Electroencephalographic (EEG) analysis has been widely adopted for the monitoring of cognitive state changes and sleep stages because abundant information in EEG recording reflects changes in drowsiness, arousal, sleep, and attention, etc. In this study, micro-electro-mechanical systems (MEMS) based silicon spiked electrode array, namely dry electrodes, are fabricated and characterized to bring EEG monitoring to the operational workplaces without requiring conductive paste or scalp preparation. An isotropic/anisotropic reactive ion etching with inductive coupled plasma (RIE-ICP) micromachining fabrication process was developed to manufacture the needle-like micro probes to pierce the stratum corneum of skin and obtain superior electrically conducting characteristics. This article reports a series of prosperity testing and evaluations of continuous EEG recordings. Our results suggest that the dry electrodes have advantages in electrode-skin interface impedance, signal intensity and size over the conventional (wet) electrodes. In addition, we also developed an EEG-based drowsiness estimation system that consists of the dry-electrode array, power spectrum estimation, principal component analysis (PCA)-based EEG signal analysis, and multivariate linear regression to estimate driverpsilas drowsiness level in a virtual-reality-based dynamic driving simulator to demonstrate the potential applications of the MEMS electrodes in operational environments.