Academic Journal
Adoption of Deep Learning Methods for SSVEP Classification in XR-Based Wearable Brain-Computer Interfaces.
| Τίτλος: | Adoption of Deep Learning Methods for SSVEP Classification in XR-Based Wearable Brain-Computer Interfaces. |
|---|---|
| Συγγραφείς: | Angrisani L; Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio n. 21, 80125 Naples, Italy., De Benedetto E; Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio n. 21, 80125 Naples, Italy., De Maria A; Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio n. 21, 80125 Naples, Italy., Duraccio L; Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio n. 21, 80125 Naples, Italy., Tedesco A; Department of Public Health, University of Naples Federico II, Via Pansini, 80131 Naples, Italy. |
| Πηγή: | Sensors (Basel, Switzerland) [Sensors (Basel)] 2026 Aug 12; Vol. 26 (16). Date of Electronic Publication: 2026 Aug 12. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI, c2000- |
| Ιατρικοί όροι (MeSH): | Evoked Potentials, Visual*/physiology , Brain-Computer Interfaces* , Deep Learning* , Wearable Electronic Devices*, Humans ; Electroencephalography ; Algorithms ; Signal Processing, Computer-Assisted |
| Περίληψη: | This paper addresses steady-state visually evoked potential (SSVEP) classification in wearable extended-reality (XR) brain-computer interfaces (BCIs), with a threefold objective. First, it investigates the effectiveness of deep learning (DL)-based SSVEP classification under XR stimulation, where platform-dependent rendering, optical see-through visualization, reduced luminance contrast, and interaction with the real environment may degrade the quality of the elicited EEG response. Second, a metrology-based performance assessment is proposed according to the Guide to the Expression of Uncertainty in Measurement (GUM), with classification accuracy and information transfer rate (ITR) expressed as best estimates with associated standard uncertainties. Finally, EEG channel reduction is analyzed toward lightweight XR-SSVEP implementations. As a representative SSVEP-specific DL model, the SSVEP time-frequency fusion network (SSVEP-TFFNet) is evaluated on an open XR benchmark dataset comprising 30 subjects and 1200 trials acquired using Microsoft HoloLens 2. A subject-independent comparison with filter-bank canonical correlation analysis (FBCCA) is performed, while intra- and inter-subject variability are incorporated into the uncertainty evaluation. Results show that SSVEP-TFFNet outperforms FBCCA under the considered XR conditions. Moreover, reduced 6- and 4-channel configurations preserve performance close to the full 8-channel montage. These findings provide evidence of the potential of suitably selected DL models for XR-based SSVEP classification and support uncertainty-aware, reduced-electrode wearable implementations. |
| References: | Front Neurorobot. 2020 Jun 03;14:25. (PMID: 32581758) Sensors (Basel). 2024 Nov 03;24(21):. (PMID: 39517980) J Neural Eng. 2011 Jun;8(3):036027. (PMID: 21566275) Front Neurosci. 2025 Sep 29;19:1679451. (PMID: 41089660) Comput Biol Med. 2022 Feb;141:105042. (PMID: 34802710) Brain Sci. 2020 Sep 28;10(10):. (PMID: 32998379) Sensors (Basel). 2026 Jan 23;26(3):. (PMID: 41682282) IEEE Trans Vis Comput Graph. 2020 Mar;26(3):1608-1621. (PMID: 30295623) J Neural Eng. 2015 Aug;12(4):046008. (PMID: 26035476) IEEE Trans Biomed Eng. 2011 Jan;58(1):200-6. (PMID: 20729160) Exp Brain Res. 2001 Apr;137(3-4):346-53. (PMID: 11355381) J Neural Eng. 2022 May 13;19(3):. (PMID: 35477130) IEEE Trans Neural Syst Rehabil Eng. 2006 Jun;14(2):234-9. (PMID: 16792302) Brain Inform. 2023 Aug 4;10(1):20. (PMID: 37540385) J Neural Eng. 2020 Feb 18;17(1):016066. (PMID: 31614342) Sensors (Basel). 2023 Jun 28;23(13):. (PMID: 37447849) |
| Contributed Indexing: | Keywords: augmented reality; brain–computer interface (BCI); deep learning; extended reality (XR); measurement; steady-state visually evoked potential (SSVEP); uncertainty; wearable systems |
| Entry Date(s): | Date Created: 20260827 Date Completed: 20260827 Latest Revision: 20260829 |
| Update Code: | 20260829 |
| PubMed Central ID: | PMC13517239 |
| DOI: | 10.3390/s26165102 |
| PMID: | 42655412 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1424-8220 |
|---|---|
| DOI: | 10.3390/s26165102 |