Enhancing Detection of SSVEPs with Intermodulation Frequencies Using Individual Calibration Data.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between human brain and external devices. Recently, we proposed an intermodulation frequency-based stimulation approach to increase the number of visual stimuli that can be presented on a computer monitor. Although our recent studies have demonstrated that this approach can encode more visual stimuli by only one flickering frequency, the performance of the intermodulation frequency-based SSVEP BCI remains poor and needs further improvement. This study aims to incorporate filter bank analysis and individual SSVEP calibration data into canonical correlation analysis (CCA) to improve the detection of SSVEPs with intermodulation frequencies. Results on classification accuracy and information transfer rate (ITR) suggest that the employment of individual calibration data can significantly improve the performance of the intermodulation frequency-based SSVEP BCI.