Head-mounted wearable devices, such as mixed reality (MR) or augmented reality (AR) glasses, serve as an intuitive platform for continuous monitoring of brain activity and eye movements, such as electroencephalogram (EEG) signals, which are integral to Brain-Computer Interface (BCI) systems. Additionally, biometric-based user identification provides an alternative method for logging into wearable systems without relying on gesture detection or voice recognition, offering a hands-free and voice-free input modality. In this presentation, I will explore the integration of a closed-loop BCI system to enable a lock/unlock mechanism for wearable devices. Specifically, the talk will focus on: (1) A patent-pending sensing technology called the Watermill EEG Electrode; (2) A 3D-printed AR glasses prototype designed to induce stable and accurate steady-state visual evoked potentials (SSVEP) for modulating brain activity; (3) An offline decoding model that combines task-related component analysis and canonical correlation analysis algorithms to decrypt encoded EEG signals for biometric-based user identification.