With newly developed wireless neuroheadsets, electroencephalography (EEG) neurometrics can be incorporated into in situ and ubiquitous physiological monitoring for human mental health. As a resource constraint system providing critical health services, the EEG headset design must consider both high application fidelity and energy efficiency. However, through empirical studies with an off-the-shelf Emotiv EPOC Neuroheadset, we uncover a mismatch between lossy EEG sensor communication and high neurometric application fidelity requirements. To tackle this problem, we study how to learn the sensitivity of neurometric application fidelity to EEG data. The learned sensitivity is used to develop two algorithms: 1) an energy minimization algorithm minimizing the energy usage in EEG sampling and networking while meeting applications\u27 fidelity requirements and 2) a fidelity maximization algorithm maximizing the sum of all applications\u27 fidelities through the incorporation and optimal utilization of a limited data buffer. The effectiveness of our proposed solutions is validated through trace-driven experiments
