SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces
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Abstract (arXiv)
Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) offer a non-invasive means of communication through high-speed speller systems. However, their efficiency heavily relies on individual training data obtained during time-consuming calibration sessions. To address the challenge of data insufficiency in SSVEP-based BCIs, we present SSVEP-DAN, the first dedicated neural network model designed for aligning SSVEP data across different domains, which can encompass various sessions, subjects, or devices. Our experimental results across multiple cross-domain scenarios demonstrate SSVEP-DAN's capability to transform existing source SSVEP data into supplementary calibration data, significantly enhancing SSVEP decoding accuracy in scenarios with limited calibration data. We envision SSVEP-DAN as a catalyst for practical SSVEP-based BCI applications with minimal calibration. The source codes in this work are available at: https://github.com/CECNL/SSVEP-DAN.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 54 references that are in the map. First posted on arXiv 21 Nov 2023.
Builds on
9 of 54 references.
References · 9
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2020A Survey on Negative Transfercited by 120
- 2020Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016cited by 56
- 2018Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentialscited by 15
- 2019Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classificationcited by 12
- 2020A Deep Neural Network for SSVEP-based Brain-Computer Interfacescited by 5
- 2021Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learningcited by 4
- 2022Domain Adaptation and Generalization on Functional Medical Images: A Systematic Surveycited by 4
- 2021FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfacescited by 1
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