A Novel Turbo Detector Design for a High-Speed SSVEP-Based Brain Speller

Abstract

The past decade has witnessed the rapid development of brain-computer interfaces (BCIs). The contradiction between communication rates and tedious training processes has become one of the major barriers restricting the application of steady-state visual-evoked potential (SSVEP)-based BCIs. A turbo detector was proposed in this study to resolve this issue. The turbo detector uses the filter bank canonical correlation analysis (FBCCA) as the first-stage detector and then utilizes the soft information generated by the first-stage detector and the pool of identified data generated during use to complete the second-stage detection. This strategy allows for rapid performance improvements as the data pool size increases. A standard benchmark dataset was used to evaluate the performance of the proposed method. The results show that the turbo detector can achieve an average ITR of 130 bits/min, which is about 8% higher than FBCCA. As the size of the data pool increases, the ITR of the turbo detector could be further improved.

Filter bank temporally local multivariate synchronization index for SSVEP-based BCI

“…Figure 7 shows the effect of the number of filter banks on the recognition accuracy under different time windows ( range set to [ 2 , 10 ], FBTMSI is equivalent to TMSI when = 1). Overall, the recognition accuracy increases with the increase of the number of filter banks, but meanwhile, the computational cost increases, which affects the transmission efficiency.…”

“…When users look at a target stimulus, the SSVEP signals can be observed at the same fundamental frequency as the stimulus, as well as the harmonics of the driving stimulus [ 5 ]. Due to high information transfer rate (ITR) [ 5 , 6 ], little training [ 7 , 8 ] and high reliability [ 9 ], SSVEP has been successfully applied in various BCI applications, including virtual keyboard systems [ 10 , 11 ], brain-controlled wheelchairs [ 12 ], and robotic arm control systems [ 13 ].…”