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• W2163939660 abstract "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.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.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%.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." @default.
• W2163939660 created "2016-06-24" @default.
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• W2163939660 date "2015-06-01" @default.
• W2163939660 modified "2023-10-14" @default.
• W2163939660 title "Enhancing performances of SSVEP-based brain–computer interfaces via exploiting inter-subject information" @default.
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• W2163939660 doi "https://doi.org/10.1088/1741-2560/12/4/046006" @default.
• W2163939660 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26028259" @default.
• W2163939660 hasPublicationYear "2015" @default.
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