Adaptive time-window length based on online performance measurement in SSVEP-based BCIs

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 decides whether to change or maintain the time-window length. The proposed system was tested on 7 subjects, with on average an accuracy of 98.42% and an information transfer rate (ITR) of 70.71 bits/min, representing an ITR improvement of 19.36% compared to its non-adaptive counterpart.