Research on Encoding and Decoding Algorithms for Medium/high-frequency SSVEP-based Brain-computer Interface

‍ ‍Brain-computer interface systems （BCIs） based on steady-state visual evoked potentials （SSVEPs） usually use strong flickering stimuli with low frequencies to evoke signals with strong features. Although the relevant data processing technologies are becoming more and more mature， SSVEP systems with low frequency stimuli are still uncomfortable for using and the time for acquiring enough training samples is quite long. Increasing the frequencies of stimuli can effectively relieve the visual fatigue of the subjects. Hense the friendliness of BCI systems would be greatly improved. However， the existing brain-computer interface systems based on SSVEP with medium or high frequencies have defects such as insufficient recognizable commands， low information transfer rate （ITR）， and so on. In response to the problems listed above， this study proposed a set of coding and decoding methods containing the Code Words paradigm with blank codes and ensemble task-related component analysis （eTRCA） algorithm based on the characteristics of medium and high-frequency SSVEP， of which the applicability and scalability were studied. Four frequencies in the medium and high-frequency bands （20， 24， 30 and 40 Hz） were selected to construct brain-controlled spelling systems （BCI Spellers） respectively， and our method could independently construct 6 commands by using flickering stimulation with a single frequency. It was theoretically possible to multiply the number of identifiable targets by combining Code Words with different frequencies. A total of 10 healthy subjects participated in EEG experiments. After a specified trials of electroencephalography （EEG） data were collected， those data were preprocessed by a bandpass filter （bandwidth 18~60 Hz）. Then we used eTRCA algorithm to perform the target identification process. According to our results， the 18-target offline system achieved an averaged theoretical accuracy of 96.71 ± 1.69 % and a theoretical ITR of 86.94 ± 6.07 bits/min. The above results indicate that the codec method proposed in this study could effectively induce and accurately identify the multi-dimensional features in the time， frequency， and phase domain of medium/high-frequency SSVEP. Besides， increasing the frequency types of coding units， increasing the effective coding rate and improving the decoding algorithm are promising to further improve the system performance.