Comparative Analysis of Ear Electroencephalography (Ear-EEG) and Scalp Electroencephalography (Scalp-EEG) in Wearable Brain-computer Interfaces

Abstract

This paper compares and analyzes ear-based electroencephalography (Ear-EEG) and scalp-based electroencephalography (Scalp-EEG) in wearable brain-computer interfaces (BCIs) to examine how signal fidelity, robustness, and usability are balanced. The study evaluates signal quality (event-related potentials ERP, signal-to-noise ratio SNR), resistance to motion artifacts, comfort, wearability, and practical applicability. The results indicate that, despite moderate signal attenuation (amplitude loss of 21% to 44% compared to optimized Scalp-EEG) and limited spatial coverage (1–6 channels), Ear-EEG still achieves clinically relevant sensitivity for key auditory ERP components (Hedges' *g* = 0.25–0.77) and alpha-band oscillations. Ear-EEG has inherent resistance to ocular artifacts but is highly sensitive to interference from jaw/head movements. In terms of usability metrics, Ear-EEG significantly outperforms Scalp-EEG: the dry electrode design supports over 40 hours of continuous wear with minimal discomfort (only approximately 15% of users reported noticeable foreign body sensation), can be self-installed within 5 minutes, and has approximately 45% higher social acceptability. However, Scalp-EEG still holds advantages in whole-brain coverage, high-fidelity tasks (such as N400 semantic decoding), and motion robustness during walking (no artifacts at 3.0 km/h). Additionally, this paper demonstrates the feasibility of Ear-EEG for mobile, long-term monitoring applications (such as sleep tracking and epilepsy detection), while also clarifying the unique application scenarios where Scalp-EEG remains irreplaceable.