Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information
Related Experiment Video
Updated: Apr 11, 2026
SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
Published on: November 24, 2015
Peng Yuan1, Xiaogang Chen1, Yijun Wang2,3
1Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, People's Republic of China.
Objective:
A new training-free framework was proposed for target detection in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) using joint frequency-phase coding.
Approach:
The key idea is to transfer SSVEP templates from the existing subjects to a new subject to enhance the detection of SSVEPs. Under this framework, transfer template-based canonical correlation analysis (tt-CCA) methods were developed for single-channel and multi-channel conditions respectively. In addition, an online transfer template-based CCA (ott-CCA) method was proposed to update EEG templates by online adaptation.
Main Results:
The efficiency of the proposed framework was proved with a simulated BCI experiment. Compared with the standard CCA method, tt-CCA obtained an 18.78% increase of accuracy with a data length of 1.5 s. A simulated test of ott-CCA further received an accuracy increase of 2.99%.
Significance:
The proposed simple yet efficient framework significantly facilitates the use of SSVEP BCIs using joint frequency-phase coding. This study also sheds light on the benefits from exploring and exploiting inter-subject information to the electroencephalogram (EEG)-based BCIs.
