@conference {15763,
	title = {User-Specific Channel Selection Method to Improve SSVEP BCI Decoding Robustness Against Variable Inter-Stimulus Distance},
	booktitle = {2019 IEEE EMBS Neural Engineering (NER) Conference},
	year = {2019},
	abstract = {Steady-state visual evoked potentials (SSVEP) are responses elicited when a user is presented with a repetitive visual stimulus. Change in stimuli proximity has been shown to have an influence on the performance of SSVEP-based BCI, where the inter-stimulus distance has a positive correlation with the overall performance. This limits the flexibility in stimulus design by imposing a constraint on the acceptable inter-stimulus distance, consequently limiting the range of applicability for SSVEP-based BCIs in real-world applications. Another limitation that needs to be addressed is the required number of EEG channels. In this study, we investigated these two challenges. A process of selecting optimal user-specific channel set was proposed. We demonstrated that the user-specific channel set is more robust against variable inter-stimulus distance. A significant improvement in accuracy (p=10\&nbsp;<sup>-3</sup>\&nbsp;) of 5\% and a reduction in variation (p=10\&nbsp;<sup>-3</sup>\&nbsp;) of 55\% was achieved on average when compared to the performance using the classic 3-channel set (O1, O2, Oz) and 6-channel set (O1, O2, Oz, PO3, PO4, POz).},
	url = {https://ieeexplore.ieee.org/abstract/document/8716912},
	author = {Aravind Ravi and Sarah Pearce and Xin Zhang and Ning Jiang}
}
