Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information

• Published in: Journal of neural engineering Vol. 12; no. 4; pp. 46006 - 46017
• Main Authors: Yuan, Peng, Chen, Xiaogang, Wang, Yijun, Gao, Xiaorong, Gao, Shangkai
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.08.2015
• Subjects: Adaptation; Algorithms; Brain-computer interface (BCI); Brain-Computer Interfaces; Coding; Data Interpretation, Statistical; Electroencephalography; Electroencephalography - methods; Evoked Potentials, Visual - physiology; Human-computer interface; Humans; inter-subject information; Online; online adaptation; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Simulation; steady-state visual evoked potential (SSVEP); template; Visual; Visual Cortex - physiology
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2560/12/4/046006

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.