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In the steady-state visual evoked potentials (SSVEP)-based brain–computer interfaces (BCIs), the time-window length plays an important role as it controls how much data is used each time in signal processing and classification for target detection. Normally, the larger the time-window length, the higher the detection accuracy and the longer the detection time, while the overall performance of a BCI system involves a trade-off between the detection accuracy and the detection time. An optimal time-window length is thus preferred but unfortunately such a value varies considerably among different subjects. This paper proposes an adaptive method to optimize the time-window length based on the subject׳s online performance. More specifically, a feedback from the subject using two commands, “Undo” and “Delete”, is designed to assess the performance in real time. Based on the assessment, the adaptive mechanism …