Abstract
A crucial problem for the overall performance of steady-state visual evoked potentials (SSVEP)-based brain computer interface (BCIs) is the right choice of the time-window length since a large window results in a higher accuracy but longer detection time, making the system impractical. This paper proposes an adaptive time window length to improve the system performance based on the subject’s online performance. However, since there is no known methods of assessing the online performance in real time, it is also proposed a feedback from the user, through a speller, for the system to know whether the output is correct or not and change or maintain the time-window length accordantly. The system was implemented fully online and tested in 4 subjects. The subjects have attained an average information transfer rate (ITR) of 62.09bit/min and standard deviation of 2.13bit/min with a mean accuracy of 99.00% and standard deviation of 1.15%, which represents an improvement of about 6.50% of the ITR to the fixed time-window length system.
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da Cruz, J.N., Wong, C.M., Wan, F. (2013). An SSVEP-Based BCI with Adaptive Time-Window Length. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_38
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DOI: https://doi.org/10.1007/978-3-642-39068-5_38
Publisher Name: Springer, Berlin, Heidelberg
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