Parvez, M.Z. and Paul, M., 2016. Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(1), pp.158-168.
Fergus, P., Hussain, A., Hignett, D., Al-Jumeily, D., Abdel-Aziz, K. and Hamdan, H., 2016. A machine learning system for automated whole-brain seizure detection. Applied Computing and Informatics, 12(1), pp.70-89.
Bhardwaj, A., Tiwari, A., Krishna, R. and Varma, V., 2016. A novel genetic programming approach for epileptic seizure detection. Computer methods and programs in biomedicine, 124, pp.2-18.
Gómez, C., Poza, J., Gutiérrez, M.T., Prada, E., Mendoza, N. and Hornero, R., 2016. Characterization of EEG patterns in brain-injured subjects and controls after a Snoezelen® intervention. computer methods and programs in biomedicine, 136, pp.1-9.
Ghaderyan, P. and Abbasi, A., 2016. An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations. International Journal of Psychophysiology, 110, pp.91-101.
Shiao, H.T., Cherkassky, V., Lee, J., Veber, B., Patterson, N., Brinkmann, B. and Worrell, G., 2016. SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal. IEEE Transactions on Biomedical Engineering.
Babu, U.R. and Sridhar, C.N.V., Design and Classification of EEG and ECG Signals for Detection of Seizures based on Prototype Recognition.
Parvez, Mohammad Zavid, Manoranjan Paul, and Michael Antolovich. "Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals." Journal of Medical and Bioengineering Vol 4.2 (2015).
Bhardwaj, Arpit, et al. "Classification of EEG signals using a novel genetic programming approach." Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion. ACM, 2014.
Ghaderyan, Peyvand, Ataollah Abbasi, and Mohammad Hossein Sedaaghi. "An efficient seizure prediction method using KNN-based undersampling and linear frequency measures." Journal of neuroscience methods (2014).