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  • Title: Enhancing performances of SSVEP-based brain-computer interfaces via exploiting inter-subject information.
    Author: Yuan P, Chen X, Wang Y, Gao X, Gao S.
    Journal: J Neural Eng; 2015 Aug; 12(4):046006. PubMed ID: 26028259.
    Abstract:
    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.
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