Development of a Measurement Procedure for Emotional States Detection Based on Single-Channel Ear-EEG: A Proof-of-Concept Study.

Real-time emotion monitoring is increasingly relevant in healthcare, automotive, and workplace applications, where adaptive systems can enhance user experience and well-being. This study investigates the feasibility of classifying emotions along the valence-arousal dimensions of the Circumplex Model of Affect using EEG signals acquired from a single mastoid channel positioned near the ear. Twenty-four participants viewed emotion-eliciting videos and self-reported their affective states using the Self-Assessment Manikin. EEG data were recorded with an OpenBCI Cyton board and both spectral and temporal features (including power in multiple frequency bands and entropy-based complexity measures) were extracted from the single ear-channel. A dual analytical framework was adopted: classical statistical analyses (ANOVA, Mann-Whitney U) and artificial neural networks combined with explainable AI methods (Gradient × Input, Integrated Gradients) were used to identify features associated with valence and arousal. Results confirmed the physiological validity of single-channel ear-EEG, and showed that absolute β- and γ-band power, spectral ratios, and entropy-based metrics consistently contributed to emotion classification. Overall, the findings demonstrate that reliable and interpretable affective information can be extracted from minimal EEG configurations, supporting their potential for wearable, real-world emotion monitoring. Nonetheless, practical considerations-such as long-term comfort, stability, and wearability of ear-EEG devices-remain important challenges and motivate future research on sustained use in naturalistic environments.