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[8] Bhat S, Acharya UR, Adeli H, Bairy GM, Adeli A, “Automated diagnosis of autism: In search of a mathematical marker”, Reviews in the neurosciences, vol. 25, issue 6, Aug 2014. [9] Neha Jain, Shishir Kumar, Amit Kumar, Pourya Shamsolmoali, Masoumeh Zareapoor, “Hybrid deep neural networks for face emotion recognition”, Pattern Recognition Letters, vol. 115, issue 1, Nov 2018, pp. 101-106. [10] Ofer GolanEmail, Yana Sinai-Gavrilov, Simon Baron-Cohen, The Cambridge Mindreading Face-Voice Battery for Children (CAM-C): complex emotion recognition in children with and without autism spectrum conditions”, Molecular Autism, vol. 6, issue 1, April 2015. [11] Monita Chatterjee, Danielle J. Zion, Mickael L. Deroche, Brooke A. Burianek, Charles J. Limb, Alison P. Goren, Aditya M. Kulkarni, Julie A. Christensen, Voice emotion recognition by cochlear-implanted children and their normally-hearing peers”, Hearing Research, vol. 322, April 2015, pp. 151-162. [12] Mingmin Zhao, Fadel Adib, Dina Katabi, Emotion Recognition Using Wireless Signals”, Communications of the ACM, vol. 61, issue 9, Sep 2018, pp. 91-100. [13] Mika Lehtinen, Kimmo Forsman, Jaakko Malmivuo, Hannu Eskola, " Effects of skull and scalp thickness on EEG", Proceedings of the 1 st International Conference on Bioelectromanetism, vol. 34, Supplement 1, Part 2, 1996. [14] S. Koelstra, C. Muehl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, I. Patras, "DEAP: A Database for Emotion Analysis using Physiological Signals,", IEEE Trans. on Affective Computing, vol. 3, issue 1, Jan.-March 2012. [15] J. Russel, "A Circumplex Model of Affect," Journal of Personality and Social Psychology, vol. 39, issue. 6, Dec 1980, pp. 1161-1178. [16] Henry Candra, Mitchell Yuwono, Rifai Chai, Ardi Handojoseno, Irraivan Elamvazuthi, Hung T. Nguyen, Steven Su, ”Investigation of Window Size in Classification of EEG-Emotion Signal with Wavelet Entropy and Support Vector Machine”, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015. [17] B.fron et al., "Least Angle Regression", The Annals of Statistics, 2004. , pp. 411-416. [18] Manish Sharma, P. V. Achuth, Dipankar Deb, Subha D. Puthankattil, U. RajendraAcharya, “An automated diagnosis of depression using three-channel bandwidth-duration localized wavelet filter bank with EEG signals”, Cognitive Systems Research, vol. 52, Dec 2018, pp. 508-520. [19] J.B. Henriques, R.J. Davidson, “Left frontal hypoactivation in depression”, Journal of Abnormal Psychology, vol. 100, issue 4, 1991, pp. 535-545. [20] R.J. Davidson, “Anterior electrophysiological asymmetries, emotion, and depression: Conceptual and methodological conundrums”, Psychophysiology, vol. 35, issue 5, 1998, pp. 607-614. [21] G.C. Blackhart, J.A. Minnix, J.P. Kline, “Can EEG asymmetry patterns predict future development of anxiety and depression?”, Biological Psychology, vol. 72, issue 1, 2006, pp. 46-50. Y.Tsurugasaki etal. / IdentiïŹcationofEffectiveEEGElectrodes forDepressionSensing 161
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Intelligent Environments 2019 Workshop Proceedings of the 15th International Conference on Intelligent Environments
Titel
Intelligent Environments 2019
Untertitel
Workshop Proceedings of the 15th International Conference on Intelligent Environments
Autoren
Andrés Muñoz
Sofia Ouhbi
Wolfgang Minker
Loubna Echabbi
Miguel Navarro-CĂ­a
Verlag
IOS Press BV
Datum
2019
Sprache
deutsch
Lizenz
CC BY-NC 4.0
ISBN
978-1-61499-983-6
Abmessungen
16.0 x 24.0 cm
Seiten
416
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Intelligent Environments 2019