@conference {29415,
	title = {Asynchronous SSMVEP BCI and Influence of Dynamic Background in Augmented Reality},
	booktitle = {The 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP 2021)},
	year = {2021},
	abstract = {One of the important challenges in designing an SSMVEP-based Brain-computer interface (BCI) under any augmented reality (AR) environment is the stability of the system under dynamically changing background conditions. In this study, a novel AR optical see-through (OST) light-weight headset was incorporated into a BCI system based on steady state-motion-visually evoked potential (SSMVEP). The AR-BCI system was evaluated in a five-class asynchronous scenario, with four intentional control (IC) states and one no-control (NC) state. The system was tested under two background conditions: active (AB) and non-active (NB) background. Offline analysis was performed to evaluate the IC vs. NC classification performance using the complex-spectrum based convolutional neural network (C-CNN). The average performance metrics for 1-second window length were (NB vs. AB): Accuracy: 83\%{\textpm}9\% vs. 81\%{\textpm}8\%, F1-score: 0.82{\textpm}0.1 vs. 0.80{\textpm}0.09 and False Activation Rate: 16\%{\textpm}11\% vs. 17\%{\textpm}10\%. The results suggested that the C-CNN method applied on the SSMVEP responses in AR provides high IC vs. NC classification performance, and more importantly, is robust to change in dynamically changing background conditions.},
	url = {https://doi.org/10.1109/M2VIP49856.2021.9665078},
	author = {Aravind Ravi and Jing Lu and Sarah Pearce and Ning Jiang}
}
