Next Article in Journal
Volatile Profiling and Variety Discrimination of Leather Using GC-IMS Coupled with Chemometric Analysis
Previous Article in Journal
Rotation-Sensitive Feature Enhancement Network for Oriented Object Detection in Remote Sensing Images
error_outline You can access the new MDPI.com website here. Explore and share your feedback with us.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sensors
Volume 26
Issue 2
10.3390/s26020385
sensors-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Marco Arnesano
1,
Pasquale Arpaia
2,*,
Simone Balatti
3,
Gloria Cosoli
1,*,
Matteo De Luca
4,
Ludovica Gargiulo
5,
Nicola Moccaldi
2,
Andrea Pollastro
2,
Theodore Zanto
6 and
Antonio Forenza
3
1
Department of Theoretical and Applied Sciences, eCampus University, 22060 Novedrate, Italy
2
Department of Electrical Engineering and Information Technology, University of Naples “Federico II”, 80121 Naples, Italy
3
AWEAR Technologies Inc., San Francisco, CA 94105, USA
4
Department of Advanced Biomedical Science, University of Naples “Federico II”, 80131 Naples, Italy
5
National Research Council (STIIMA-CNR), 23900 Lecco, Italy
6
UCSF Weill Institute for Neurosciences School, San Francisco, CA 94143, USA
*
Authors to whom correspondence should be addressed.
Sensors 2026, 26(2), 385; https://doi.org/10.3390/s26020385
Submission received: 13 November 2025 / Revised: 19 December 2025 / Accepted: 29 December 2025 / Published: 7 January 2026
(This article belongs to the Section Wearables)
Versions Notes

Abstract

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.
Keywords: EEG; ear-EEG; wearable EEG; emotion recognition; single-channel; physiological measurement; signal processing EEG; ear-EEG; wearable EEG; emotion recognition; single-channel; physiological measurement; signal processing

Share and Cite

MDPI and ACS Style

Arnesano, M.; Arpaia, P.; Balatti, S.; Cosoli, G.; Luca, M.D.; Gargiulo, L.; Moccaldi, N.; Pollastro, A.; Zanto, T.; Forenza, A. Development of a Measurement Procedure for Emotional States Detection Based on Single-Channel Ear-EEG: A Proof-of-Concept Study. Sensors 2026, 26, 385. https://doi.org/10.3390/s26020385

AMA Style

Arnesano M, Arpaia P, Balatti S, Cosoli G, Luca MD, Gargiulo L, Moccaldi N, Pollastro A, Zanto T, Forenza A. Development of a Measurement Procedure for Emotional States Detection Based on Single-Channel Ear-EEG: A Proof-of-Concept Study. Sensors. 2026; 26(2):385. https://doi.org/10.3390/s26020385

Chicago/Turabian Style

Arnesano, Marco, Pasquale Arpaia, Simone Balatti, Gloria Cosoli, Matteo De Luca, Ludovica Gargiulo, Nicola Moccaldi, Andrea Pollastro, Theodore Zanto, and Antonio Forenza. 2026. "Development of a Measurement Procedure for Emotional States Detection Based on Single-Channel Ear-EEG: A Proof-of-Concept Study" Sensors 26, no. 2: 385. https://doi.org/10.3390/s26020385

APA Style

Arnesano, M., Arpaia, P., Balatti, S., Cosoli, G., Luca, M. D., Gargiulo, L., Moccaldi, N., Pollastro, A., Zanto, T., & Forenza, A. (2026). Development of a Measurement Procedure for Emotional States Detection Based on Single-Channel Ear-EEG: A Proof-of-Concept Study. Sensors, 26(2), 385. https://doi.org/10.3390/s26020385

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop