Deep Learning Decoding of Steady-State Visual Evoked Potential (SSVEP) for Real-Time Mobile Brain–Computer Interfaces: A Narrative Review from Laboratory Settings to Lightweight Engineering Applications

: This review provides a comprehensive overview of SSVEP deep learning decoding technologies for mobile applications, establishing a technical foundation to advance mobile brain-computer interfaces from laboratory settings to practical deployment.

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Publication Details

Journal
Brain Sciences
Published
2026-03-31
DOI
https://doi.org/10.3390/brainsci16040387
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00

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article

Deep Learning Decoding of Steady-State Visual Evoked Potential (SSVEP) for Real-Time Mobile Brain–Computer Interfaces: A Narrative Review from Laboratory Settings to Lightweight Engineering Applications

Chunjing Tao, Hanzhen Zhang
Brain Sciences
EEG and Brain-Computer Interfaces
article

Deep Learning Decoding of Steady-State Visual Evoked Potential (SSVEP) for Real-Time Mobile Brain–Computer Interfaces: A Narrative Review from Laboratory Settings to Lightweight Engineering Applications

Chunjing Tao, Hanzhen Zhang
article en

Abstract

: This review provides a comprehensive overview of SSVEP deep learning decoding technologies for mobile applications, establishing a technical foundation to advance mobile brain-computer interfaces from laboratory settings to practical deployment.

Brain SciencesVol. 16(4)
Beihang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 37%
EEG and Brain-Computer Interfaces
DOIOpen Access PDFOpenAlex
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Deep Learning Decoding of Steady-State Visual Evoked Potential (SSVEP) for Real-Time Mobile Brain–Computer Interfaces: A Narrative Review from Laboratory Settings to Lightweight Engineering Applications — Chunjing Tao, Hanzhen Zhang · Brain Sciences (2026) | TGRS Research Map | TGRS