Human Communication Technology. Группа авторов

novel feature extraction technique for automated detection of focal and non-focal electroencephalogram signals. IET Sci. Meas. Technol., 11, 8, 1014–1021, 2017.

      61. Rejer, I., Genetic algorithms in EEG feature selection for the classification of movements of the left and right hand. Adv. Intell. Syst. Comput., 226, 9–11, 2013.

      62. Alın, A., Kurt, S., Mcintosh, A.R., Ozg, M., Partial least squares analysis in electrical brain activity. J. Data Sci., 7, 99–110, 2009.

      63. O’Brien, P., A primer on the discrete Fourier transform. Am. J. EEG Technol., 34, 4, 190–223, 2018.

      64. Birvinskas, D., Jusas, V., Martišius, I., Damaševičius, R., Data compression of EEG signals for artificial neural network classification. Inf. Technol. Control, 42, 3, 238–241, 2013.

      65. Chaurasiya, R.K., Londhe, N.D., Ghosh, S., Statistical wavelet features, PCA, and SVM based approach for EEG signals classification. World Acad. Sci. Eng. Technol. Int. J. Electr. Comput. Energy Electron. Commun. Eng., 9, 2, 182–186, 2015.

      66. Kim, M. and Chang, S., A consumer transceiver for long-range IoT communications in emergency environments. IEEE Trans. Consum. Electron., 62, 3, 226–234, 2016.

      67. Abdellatif, A.A., Khafagy, M.G., Mohamed, A., Chiasserini, C.F., EEG-based transceiver design with data decomposition for healthcare IoT applications. IEEE Internet Things J., 5, 5, 3569–579, 2018.

      68. Fisher, R.S. et al., Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE). Epilepsia, 46, 4, 1–3, 2005.

      69. Parvez, M.Z., Paul, M., Antolovich, M., Detection of pre-stage of epileptic seizure by exploiting temporal correlation of EMD decomposed EEG signals. J. Med. Bioeng., 4, 2, 110–116, 2015.

      70. Abdulhay, E., Alafeef, M., Abdelhay, A., Al-Bashir, A., Classification of normal, ictal and inter-ictal EEG via direct quadrature and random forest tree. J. Med. Biol. Eng., 37, 6, 843–857, 2017.

      71. Qaraqe, M., Ismail, M., Serpedin, E., Band-sensitive seizure onset detection via CSP enhanced EEG features. Epilepsy Behav., 50, 77–87, 2015.

      72. Mutlu, A.Y., Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition. Biomed. Signal Process. Control, 40, 33–40, 2018.

      73. Diykh, M., Li, Y., Wen, P., Classify epileptic EEG signals using weighted complex networks based community structure detection. Expert Syst. Appl., 90, 87–100, 2017.

      74. Birjandtalab, J., Baran Pouyan, M., Cogan, D., Nourani, M., Harvey, J., Automated seizure detection using limited-channel EEG and non-linear dimension reduction. Comput. Biol. Med., 82, 49–58, 2017.

      75. Albert, B. et al., Automatic EEG processing for the early diagnosis of traumatic brain injury. Proc. Comput. Sci., 96, 703–712, 2016.

      76. Variane, G.F.T. et al., Early amplitude-integrated electroencephalography for monitoring neonates at high risk for brain injury. J. Pediatr. (Rio. J), 93, 5, 460–466, 2017.

      77. Franke, L.M., Walker, W.C., Hoke, K.W., Wares, J.R., Distinction in EEG slow oscillations between chronic mild traumatic brain injury and PTSD. Int. J. Psychophysiol., 106, 21–29, 2016.

      78. Weeke, L.C. et al., Role of EEG background activity, seizure burden and MRI in predicting neurodevelopmental outcome in full-term infants with hypoxicischaemic encephalopathy in the era of therapeutic hypothermia. Eur. J. Paediatr. Neurol., 20, 6, 855–864, 2016.

      79. Nevalainen, P. et al., Evoked potentials recorded during routine EEG predict outcome after perinatal asphyxia. Clin. Neurophysiol., 128, 7, 1337–1343, 2017.

      80. Subramanian, R.R. and Seshadri, K., Design and Evaluation of a Hybrid Hierarchical Feature Tree Based Authorship Inference Technique, in: Advances in Data and Information Sciences. Lecture Notes in Networks and Systems, Kolhe, M., Trivedi, M., Tiwari, S., Singh, V. (Eds.), p. 39, 2019.

      81. Joshva Devadas, T. and Raja Subramanian, R., Paradigms for Intelligent IoT Architecture, in: Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm. Intelligent Systems Reference Library, Peng, S.L., Pal, S., Huang, L. (Eds.), p. 174, 2020.

      1 *Corresponding author: [email protected]