Reliability-based automatic repeat request for short code modulation visual evoked potentials in brain computer interfaces

We propose two methods to improve code modulation visual evoked potential brain computer interfaces (cVEP BCIs). Most of BCIs average brain signals from several trials in order to improve the classification performance. The number of averaging defines the trade-off between input speed and accuracy, and the optimal averaging number depends on individual, signal acquisition system, and so forth. Firstly, we propose a novel dynamic method to estimate the averaging number for cVEP BCIs. The proposed method is based on the automatic repeat request (ARQ) that is used in communication systems. The existing cVEP BCIs employ rather longer code, such as 63-bit M-sequence. The code length also defines the trade-off between input speed and accuracy. Since the reliability of the proposed BCI can be controlled by the proposed ARQ method, we introduce shorter codes, 32-bit M-sequence and the Kasami-sequence. Thanks to combine the dynamic averaging number estimation method and the shorter codes, the proposed system exhibited higher information transfer rate compared to existing cVEP BCIs.