A statistical framework for EEG channel selection and seizure prediction on mobile

Aarabi, A., & He, B. (2012). A rule-based seizure prediction method for focal neocortical epilepsy. Clinical Neurophysiology, 123, 1111–1122.

Abd El-Samie, F. E. (2011). Information security for automatic speaker identification (1st ed.). New York: Springer.

Aiupkumar, B., Bej, T., & Agarwal, S. (2013) Comparison study of lossless data compression algorithms for text data. IOSR Journal of Computer Engineering, 11, 15–19.

Alickovic, E., Kevric, J., & Subasi, A. (2018). Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction. Biomedical Signal Processing and Control, 39, 94–102.

Berger, H. (1929). Über des Elekrenkephalogramm des Menschen. ArchivfürPsychiatrie und Nervenkrankheiten, 87(1), 527–570.

Chiang, C. Y., Chang, N. F., Chen, T. C., Chen, H. H., & Chen, L. G. (2011). Seizure prediction based on classification of EEG synchronization patterns with on-line retraining and post-processing scheme. International Conference of the IEEE EMBS. https://doi.org/10.1109/IEMBS.2011.6091865 .

Costa, R. P., Oliveira, P., Rodrigues, G., Direito, B., & Dourado, A. (2008). Epileptic seizure classification using neural networks with 14 features. Proceedings of KES. https://doi.org/10.1007/978-3-540-85565-1_35

Gadhoumi, K., Lina, J. M., & Gotman, J. (2013). Seizure Prediction in patients with mesial temporal lobe epilepsy using EEG measures of state similarity. Clinical Neurophysiology, 124, 1745–1754.

Hung, S. H., Chao, C. F., Wang, S. K., Lin, B. S., & Lin, C. T. (2010). VLSI implementation for epileptic seizure prediction system based on wavelet and Chaos theory. In Proceedings of the IEEE TENCON.

Kannan, R. S., & Eswaran, C. (2007). Lossless compression schemes for EEG signals using neural network predictors. EURASIP Journal on Advances in Signal Processing, 2007, 102.

Kopsinis, Y., & McLaughlin, S. (2009). Development of EMD-based denoising methods inspired by wavelet thresholding. IEEE Transactions on Signal Processing, 57(4), 1351–1362.

Kuo, S. M., Lee, B. H., & Tian, W. (2006). Real-time digital signal processing, implementations and applications. New York: Wiley.

Li, S., Zhou, W., Yuan, Q., & Liu, Y. (2013). Seizure prediction using spike rate of intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(6), 880–886.

Liu, Y., Li, Y., Lin, H., & Ma, H. (2014). An amplitude-preserved time–frequency peak filtering based on empirical mode decomposition for seismic random noise reduction. IEEE Geoscience and Remote Sensing Letters, 11(5), 896–900.

Makeig, S., Kothe, C., Mullen, T., Bigdely-Shamlo, N., Zhang, Z., & Kreutz-Delgado, K. (2012) Evolving signal processing for brain–computer interfaces. Proceedings of the IEEE, 100, 1567–1584.

Milić, L. D., Lutovac, M. D., & Ćertić, J. D. (2013). Design of first–order differentiator utilising FIR and IIR sub–filters. International Journal of Reasoning-based Intelligent, Systems, 5(1), 3–11.

Miri, M. R., & Nasrabadi, A. M. (2011). A new seizure prediction method based on return map. In Proceedings of the Iranian Conference on BioMedical Engineering, Tehran.

Qi, Y., Wang, Y., Zheng, X., Zhang, J., Zhu, J., & Guo, J. (2012). Efficient epileptic seizure detection by a combined IMF-VoE feature. In Proceedings of the International Conference of the IEEE EMBS.

Rishita, S., & Shahare, P. (2017). Digital image compression using hybrid scheme using DWT and quantization with DCT for still digital image. International Research Journal of Engineering and Technology. e-ISSN: 2395-0056, p-ISSN: 2395–0072, .

Ruchi, G., Kumar, M., & Bathla, R. (2016). Data compression—Lossless and lossy techniques. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 5(7), 120–125.

Schelter, B., Drentrup, H. F., Ihle, M., Bonhage, A. S., & Timmer, J. (2011). Seizure prediction in epilepsy: from circadian concepts via probabilistic forecasting to statistical evaluation. In Proceedings of the IEEE International Conference of IEEE EMBS.

Scherer, R., Moitzi, G., Daly, I., & Müller-Putz, G. R. (2013). On the use of games for noninvasive EEG-based functional brain mapping. IEEE Transactions on Computational Intelligence and AI in Games, 5(2), 155–163.

Sriraam, N. (2012). A high-performance lossless compression scheme for EEG signals using wavelet transform and neural network predictors. International Journal of Telemedicine and Applications. https://doi.org/10.1155/2012/302581 .

Thurman, D. J., Beghi, E., Begley, C. E., Berg, A. T., Buchhalter, J. R., Ding, D., et al. (2011). Standards for epidemiologic studies and surveillance of epilepsy. Epilepsia, 52(Issue Supplement s7), 1–26.

Tzallas, A. T., Tsipouras, M. G., Tsalikakis, D. G., Karvounis, E. C., Astrakas, L., Konitsiotis, S., et al. (2012). Automated epileptic seizure detection methods: a review study. In D. Stevanovic (Ed.), Epilepsy—Histological, electroencephalographic and psychological aspects. Rijeka: InTech. ISBN: 978-953-51-0082-9.

Tzimourta, K. D., Tzallas, A. T., Giannakeas, N., Astrakas, L. G., Tsalikakis, D. G., & Tsipouras, M. G. (2018). Epileptic seizures classification based on long-term EEG signal wavelet analysis. In: N. Maglaveras, I. Chouvarda, P. de Carvalho (Eds.), Precision medicine powered by pHealth and connected health (pp. 165–169). Singapore: Springer.

Wang, S., Chaovalitwongse, W. A., & Wong, S. (2010). A novel reinforcement learning framework for online adaptive seizure prediction. In Proceedings of the IEEE international conference on bioinformatics and biomedicine, Hong Kong

Wang, S., Chaovalitwongse, W. A., & Wong, S. (2013). Online seizure prediction using an adaptive learning approach. IEEE Transactions on Knowledge and Data Engineering, 25(12), 2854–2866.

Williamson, J. R., Bliss, D. W., Browne, D. W., & Narayanan, J. T. (2012). Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy & Behavior, 25, 230–238.

Xie, S., & Krishnan, S. (2011). Signal decomposition by multi-scale PCA and its applications to long-term EEG signal classification. In Proceedings of the IEEE international conference on complex medical engineering.

Yin, L., Yang, R., Gabbouj, M., & Neuvo, Y. (1996). Weighted median filters: a tutorial. IEEE Transactions on Circuits and Systems-II: Analog and Digital Signal Processing, 43(3), 157–192.

Zandi, A. S., Tafreshi, R., Javidan, M., Dumont, G. A. (2010). Predicting temporal lobe epileptic seizures based on zero-crossing interval analysis in scalp EEG. In Proceedings of the 32nd annual international conference of the IEEE EMBS.

Zandi, A. S., Tafreshi, R., Javidan, M., & Dumont, G. A. (2013). Predicting epileptic seizures in scalp EEG based on a variational bayesian gaussian mixture model of zero-crossing intervals. IEEE Transactions on Biomedical Engineering, 60(5), 1401–1413.