# SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces

arXiv: https://arxiv.org/abs/2311.12666 · HTML: https://mlsanity.com/paper/2311.12666/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 54 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016](https://mlsanity.com/paper/2004.06286/) (2020)
- [Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials](https://mlsanity.com/paper/1803.04566/) (2018)
- [Simulating Brain Signals: Creating Synthetic EEG Data via Neural-Based Generative Models for Improved SSVEP Classification](https://mlsanity.com/paper/1901.07429/) (2019)
- [A Deep Neural Network for SSVEP-based Brain-Computer Interfaces](https://mlsanity.com/paper/2011.08562/) (2020)
- [Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning](https://mlsanity.com/paper/2102.05194/) (2021)
- [Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey](https://mlsanity.com/paper/2212.03176/) (2022)
- [FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces](https://mlsanity.com/paper/2109.02165/) (2021)

## Built on by (most cited first, 1 of 1)

- [Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive Learning](https://mlsanity.com/paper/2406.16910/) (2024)

## 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.