Academic Journal
Deep Learning for Ear-EEG-Based Brain-Computer Interface: A Systematic Comparison and Design Insights.
| Τίτλος: | Deep Learning for Ear-EEG-Based Brain-Computer Interface: A Systematic Comparison and Design Insights. |
|---|---|
| Συγγραφείς: | Kim JS; Department of Computer and Information Science, Korea University, Sejong 30019, Republic of Korea., Choi SI; Medical Metrology Group, Division of Biomedical Metrology, Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, Republic of Korea., Hwang HJ; Department of Electronics and Information Engineering, Korea University, Sejong 30019, Republic of Korea.; Digital Healthcare Center, Sejong Institute for Business and Technology, Korea University, Sejong 30019, Republic of Korea.; Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong 30019, Republic of Korea., Han CH; Digital Healthcare Center, Sejong Institute for Business and Technology, Korea University, Sejong 30019, Republic of Korea.; Department of Computer Science and Software Engineering, Korea University, Sejong 30019, Republic of Korea. |
| Πηγή: | Biosensors [Biosensors (Basel)] 2026 Aug 12; Vol. 16 (8). Date of Electronic Publication: 2026 Aug 12. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: MDPI Pub Country of Publication: Switzerland NLM ID: 101609191 Publication Model: Electronic Cited Medium: Internet ISSN: 2079-6374 (Electronic) Linking ISSN: 20796374 NLM ISO Abbreviation: Biosensors (Basel) Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI Pub. |
| Ιατρικοί όροι (MeSH): | Brain-Computer Interfaces* , Deep Learning* , Electroencephalography* , Ear*, Humans ; Neural Networks, Computer ; Signal-To-Noise Ratio |
| Περίληψη: | Electroencephalography (EEG) measured inside or around ears, called ear-EEG, provides a practical measurement modality for daily brain-computer interface (BCI) applications. However, reliable decoding of mental imagery remains challenging due to the limited number of channels, low signal-to-noise ratio (SNR), and substantial inter- and intra-subject variability inherent to ear-EEG. Addressing these constraints requires advanced decoding strategies specifically optimized for this signal domain. In this study, we retrospectively analyze the ear-EEG dataset of a previous study in which a real-time endogenous BCI was evaluated using conventional machine learning. Specifically, we present an offline benchmark of 23 deep neural network architectures originally developed for scalp-EEG, which were adapted to ear-EEG and evaluated under an identical validation framework. To the best of our knowledge, this is the first systematic comparison of this breadth for ear-EEG-based mental-task classification. Beyond conventional performance comparison, we identify the optimal architecture by jointly considering statistical significance and a performance-cost trade-off, incorporating classification accuracy, parameter count, and measured computational cost. Our results demonstrate that FBLightConvNet achieves the highest classification accuracy among all evaluated models and outperforms common spatial pattern-linear discriminant analysis (CSP-LDA), a widely adopted and robust conventional baseline, on all three recording days, with the difference reaching statistical significance on Days 2 and 3. Notably, many state-of-the-art scalp-EEG models fail to generalize effectively to ear-EEG, highlighting the importance of architecture selection in this domain. These findings identify the best-performing architecture in this setting and indicate which architectural characteristics support effective ear-EEG decoding. Ultimately, this study offers practical design insights and a reproducible benchmarking framework for developing lightweight and high-performance deep learning models, which we hope will support future efforts toward real-world ear-EEG-based BCI systems. |
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| Grant Information: | No. RS-2025-25419004 National Research Foundation of Korea; No. RS-2024-00397674 National Research Foundation of Korea; IITP-2026-RS-2023-00258971 Ministry of Science and ICT |
| Contributed Indexing: | Keywords: Ear-EEG; brain–computer interface; deep learning architecture; lightweight convolutional neural network; mental imagery |
| Entry Date(s): | Date Created: 20260826 Date Completed: 20260826 Latest Revision: 20260828 |
| Update Code: | 20260828 |
| PubMed Central ID: | PMC13510306 |
| DOI: | 10.3390/bios16080437 |
| PMID: | 42645055 |
| Βάση Δεδομένων: | MEDLINE |
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