# Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning

arXiv: https://arxiv.org/abs/2102.05194 · HTML: https://mlsanity.com/paper/2102.05194/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 4 of 4)

- [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)
- [Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis](https://mlsanity.com/paper/1308.5609/) (2013)
- [Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces](https://mlsanity.com/paper/1810.02842/) (2018)

## Built on by (most cited first, 4 of 4)

- [A Hybrid Brain-Computer Interface Using Motor Imagery and SSVEP Based on Convolutional Neural Network](https://mlsanity.com/paper/2212.05289/) (2022)
- [SSVEP-DAN: A Data Alignment Network for SSVEP-based Brain Computer Interfaces](https://mlsanity.com/paper/2311.12666/) (2023)
- [iFuzzyTL: Interpretable Fuzzy Transfer Learning for SSVEP BCI System](https://mlsanity.com/paper/2410.12267/) (2024)
- [Short-length SSVEP data extension by a novel generative adversarial networks based framework](https://mlsanity.com/paper/2301.05599/) (2023)

## Abstract (arXiv)

Objective: This study aims to establish a generalized transfer-learning framework for boosting the performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) by leveraging cross-domain data transferring. Approach: We enhanced the state-of-the-art template-based SSVEP decoding through incorporating a least-squares transformation (LST)-based transfer learning to leverage calibration data across multiple domains (sessions, subjects, and EEG montages). Main results: Study results verified the efficacy of LST in obviating the variability of SSVEPs when transferring existing data across domains. Furthermore, the LST-based method achieved significantly higher SSVEP-decoding accuracy than the standard task-related component analysis (TRCA)-based method and the non-LST naive transfer-learning method. Significance: This study demonstrated the capability of the LST-based transfer learning to leverage existing data across subjects and/or devices with an in-depth investigation of its rationale and behavior in various circumstances. The proposed framework significantly improved the SSVEP decoding accuracy over the standard TRCA approach when calibration data are limited. Its performance in calibration reduction could facilitate plug-and-play SSVEP-based BCIs and further practical applications.