Update README.md

README.md

@@ -1,5 +1,5 @@
 # Transfer Learning of an Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training
-This is the official repository for the paper titled "An Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training". This repository allows you to train the ensemble of DNNs and classify the SSVEP signal using the most similar $k$ many DNNs as explained in the paper.
+This is the official repository for the paper titled "An Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training" [1] (link: https://arxiv.org/abs/2209.01511). This repository allows you to train the ensemble of DNNs and classify the SSVEP signal using the most similar $k$ many DNNs as explained in the paper.
 
 # Preparation
 The Benchmark dataset [1] and BETA dataset [2] must be downloaded. The link for the both datasets: http://bci.med.tsinghua.edu.cn/download.html.
@@ -10,10 +10,11 @@ For example, we constitute the ensemble using 34 (69) participants and test the
 PO4, PO6, POz, O1, Oz, O2).
 
 # References 
-1. Y. Wang, X. Chen, X. Gao, and S. Gao, “A benchmark dataset for
+1. O. B. Guney, H. Ozkan, "An Ensemble of DNNs for SSVEP BCI Spellers without User-Specific Training", arXiv, 2022.
+2. Y. Wang, X. Chen, X. Gao, and S. Gao, “A benchmark dataset for
    ssvep-based brain–computer interfaces,” IEEE Transactions on Neural Systems and 
    Rehabilitation Engineering,vol. 25, no. 10, pp. 1746–1752, 2016.
-2. B. Liu, X. Huang, Y. Wang, X. Chen, and X. Gao, “Beta: A large
+3. B. Liu, X. Huang, Y. Wang, X. Chen, and X. Gao, “Beta: A large
    benchmark database toward ssvep-bci application,” Frontiers in
    Neuroscience, vol. 14, p. 627, 2020.
-3. O. B. Guney, M. Oblokulov and H. Ozkan, "A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces," IEEE Transactions on Biomedical Engineering, vol. 69, no. 2, pp. 932-944,  2022.
+4. O. B. Guney, M. Oblokulov and H. Ozkan, "A Deep Neural Network for SSVEP-Based Brain-Computer Interfaces," IEEE Transactions on Biomedical Engineering, vol. 69, no. 2, pp. 932-944,  2022.