EEG Signal Processing and Machine Learning. Saeid Sanei

57: 345–359.

      6 6 Trabka, J. (1963). High frequency components in brain waves. Electroencephalography and Clinical Neurophysiology 14: 453–464.

      7 7 Cobb, W.A., Guiloff, R.J., and Cast, J. (1979). Breach rhythm: the EEG related to skull defects. Electroencephalography and Clinical Neurophysiology 47: 251–271.

      8 8 Roldan, E., Lepicovska, V., Dostalek, C., and Hrudova, L. (1981). Mu‐like EEG rhythm generation in the course of hatha‐yogi exercises. Electroencephalography and Clinical Neurophysiology 52: 13.

      9 9 IFSECN (1974). A glossary of terms commonly used by clinical electroencephalographers. Electroencephalography and Clinical Neurophysiology 37: 538–548.

      10 10 Jansen, B.H. and Rit, V.G. (1995). Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biological Cybernetics 73: 357–366.

      11 11 David, O. and Friston, K.J. (2003). A neural mass model for MEG/EEG coupling and neuronal dynamics. NeuroImage 20: 1743–1755.

      12 12 Marey, E.J. and Lippmann, G. (1876). Des variations electriques des muscles du coeur en particulier etudiees au moyen de l’electrometre di. M. Lippmann. Comptes Rendus 82: 975–977.

      13 13 Gotman, J., Ives, J.R., and Gloor, R. (1979). Automatic recognition of interictal epileptic activity in prolonged EEG recordings. Electroencephalography and Clinical Neurophysiology 46: 510–520.

      14 14 Jasper, H. (1958). Report of committee on methods of clinical exam in EEG. Electroencephalography and Clinical Neurophysiology 10: 370–375.

      15 15 Bickford, R.D. (1987). Electroencephalography. In: Encyclopedia of Neuroscience (ed. G. Adelman), 371–373. Cambridge (USA): Birkhauser.

      16 16 Montoya‐Martínez, J., Vanthornhout, J., Bertrand, A., and Francart, T. (2021). Effect of number and placement of EEG electrodes on measurement of neural tracking of speech. PLoS ONE 16 (2): e0246769.

      17 17 Collura, T. (1998). A guide to electrode selection, location, and application for EEG Biofeedback, Ohio. Proceedings of the 6th Annual Conference on Brain Function/EEG, Modification and Training, Palm Springs, CA (21–25 February).

      18 18 Nayak, D., Valentin, A., Alarcon, G. et al. (2004). Characteristics of scalp electrical fields associated with deep medial temporal epileptiform discharges. Clinical Neurophysiology 115: 1423–1435.

      19 19 Barrett, G., Blumhardt, L., Halliday, L. et al. (1976). A paradox in the lateralization of the visual evoked responses. Nature 261: 253–255.

      20 20 Halliday, A.M. (1978). Commentary: Evoked Potentials in Neurological Disorders, Chapter: Event‐Related Brain Potentials in Man (eds. E. Calloway, P. Tueting and S.H. Coslow), 197–210. Academic Press.

      21 21 Jarchi, D. and Sanei, S. (2010). Mental fatigue analysis by measuring synchronization of brain rhythms incorporating enhanced empirical mode decomposition. Proceedings of the 2nd International Workshop on Cognitive Information Processing (CIP). Elba, Italy.

      22 22 Jarchi, D. and Sanei, S. (2010). A novel method for analysis of mental fatigue from normal brain rhythms. Proceedings of the 17th European Signal Processing Conference, EUSIPCO. Denmark.

      23 23 Jarchi, D., Sanei, S., and Lorist, M.M. (2011). Coupled particle filtering: a new approach for P300‐based analysis of mental fatigue. Journal of Biomedical Signal Processing and Control 6 (2): 175–185.

      24 24 Jarchi, D., Makkiabadi, B., and Sanei, S. (2009). Estimation of trial to trial variability of P300 subcomponents by coupled Rao‐Blackwellised particle filtering. Proceedings of the IEEE Workshop on Statistical Signal Processing, SSP2009. Cardiff, UK.

      25 25 Jarchi, D., Makkiabadi, B., and Sanei, S. (2009). Separating and tracking ERP subcomponents using constrained particle filter. Proceedings of the 16th International Conference on Digital Signal Processing, DSP2009, Greece.

      26 26 Davidson, R.J. and Henriques, J.B. (2000). Regional brain function in sadness and depression. In: The Neuropsychology of Emotion (ed. J.C. Borod), 269–297. New York: Oxford Press.

      27 27 Karlin, R., Weinapple, M., Rochford, J., and Goldstein, L. (1979). Quantitated EEG features of negative affective states: report of some hypnotic studies. Research Communications in Psychology, Psychiatry, and Behavior 4: 397–413.

      28 28 Tucker, D.M., Stenslie, C.E., Roth, R.S., and Shearer, S.L. (1981). Right frontal lobe activation and right hemisphere performance decrement during a depressed mood. Archives of General Psychiatry 38 (2): 169–174.

      29 29 Foster, P.S. and Harrison, D.W. The relationship between magnitudes of cerebral activation and intensity of emotional arousal. International Journal of Neuroscience 112: 1463–1477, 2002.

      30 30 Foster, P.S. and Harrison, D.W. (2004). Cerebral correlates of varying ages of emotional memories. Cognitive and Behavioral Neurology 17 (2): 85–92.

      31 31 Demaree, H.A., Everhart, D.E., Youngstrom, E.A., and Harrison, D.W. (2005). Brain lateralization of emotional processing: historical roots and a future incorporating dominance. Behavioral and Cognitive Neuroscience Reviews 4 (1): 3–20. https://doi.org/10.1177/1534582305276837.

      32 32 Lee, G.P., Meador, K.J., Loring, D.W. et al. (2004). Neural substrates of emotion as revealed by functional magnetic resonance imaging. Cognitive and Behavioral Neurology 17 (1): 9–17.

      33 33 Xu, X., Wei, F., Zhu, Z. et al. (2020). EEG feature selection using orthogonal regression: application to emotion recognition. ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1239–1243. Barcelona, Spain. https://doi.org/10.1109/ICASSP40776.2020.9054457.

      34 34 Zheng, W.‐L., Zhu, J.‐Y., and Lu, B.‐L. (2019). Identifying stable patterns over time for emotion recognition from EEG. IEEE Transactions on Affective Computing 10 (3): 417–429.

      35 35 Costa, T., Rognoni, E., and Galati, D. (2006). EEG phase synchronization during emotional response to positive and negative film stimuli. Neuroscience Letters 406: 159–164.

      36 36 Mullin, A.P., Gokhale, A., Moreno‐De‐Luca, A. et al. (2013). Neurodevelopmental disorders: mechanisms and boundary definitions from genomes, interactomes and proteomes. Translational Psychiatry 3: e329. https://doi.org/10.1038/tp.2013.108.

      37 37 Sharbrough, F.W. (1999). Nonspecific abnormal EEG patterns, Chapter 12. In: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4e (eds. E. Niedermeyer and F.L. Da Silva). Lippincott Williams & Wilkins.

      38 38 Bancaud, J., Hecaen, H., and Lairy, G.C. (1955). Modification de la reactivite E.E.G., troubles des functions symboliques et troubles con fusionels dans les lesions hemispherigues localisees. Electroencephalography and Clinical Neurophysiology 7: 179.

      39 39 Westmoreland, B. and Klass, D. (1971). Asymetrical attention of alpha activity with arithmetical attention. Electroencephalography and Clinical Neurophysiology 31: 634–635.

      40 40 Cobb, W. (1976). EEG interpretation in clinical medicine. In: Part B, Handbook of Electroencephalography and Clinical Neurophysiology, vol. 11 (ed. A. Remond), B1–B6. Elsevier.

      41 41 Hess, R. (1975). Brain tumors and other space occupying processing. In: Part C, Handbook of Electroencephalography and Clinical Neurophysiology, vol. 14 (ed. A. Remond), C1–C6. Elsevier.

      42 42 Klass, D. and Daly, D. (eds.) (1979). Current Practice of Clinical Electroencephalography, 1e. Raven Press.

      43 43 Van Sweden, B., Wauquier, A., and Niedermeyer, E. (1999). Normal aging and transient cognitive disorders in the elderly, Chapter 18. In: Electroencephalography, Basic Principles, Clinical Applications, and Related Fields, 4e (eds. E. Niedermeyer and F.L. Da Silva), 340–348. Lippincott Williams & Wilkins.

      44 44 America Psychiatric Association (1994). Committee on Nomenclature and Statistics, Diagnostic and Statistical Manual of Mental Disorder: DSM‐IV, 4e. Washington