# Cross-Subject Transfer Learning Improves the Practicality of Real-World Applications of Brain-Computer Interfaces

arXiv: https://arxiv.org/abs/1810.02842 · HTML: https://mlsanity.com/paper/1810.02842/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2018.

## Built on by (most cited first, 2 of 2)

- [Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning](https://mlsanity.com/paper/2102.05194/) (2021)
- [Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review](https://mlsanity.com/paper/2312.07213/) (2023)

## Abstract (arXiv)

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown its robustness in facilitating high-efficiency communication. State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCIs through a calibration process. However, due to notable human variability across individuals and within individuals over time, calibration (training) data collection is non-negligible and often laborious and time-consuming, deteriorating the practicality of SSVEP BCIs in a real-world context. This study aims to develop a cross-subject transferring approach to reduce the need for collecting training data from a test user with a newly proposed least-squares transformation (LST) method. Study results show the capability of the LST in reducing the number of training templates required for a 40-class SSVEP BCI. The LST method may lead to numerous real-world applications using near-zero-training/plug-and-play high-speed SSVEP BCIs.