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A P300 potential evaluation wavelet method comparing individuals with high and low risk for alcoholism

Bibliographic reference Diniz Lopes, Carla ; Becker, Tiago ; de Jesus Kozakevicius, Alice ; A. Rasia-Filho, Alberto ; Macq, Benoît ; et. al. A P300 potential evaluation wavelet method comparing individuals with high and low risk for alcoholism. In: Neural Computing and Applications, Vol. 28, no.12, p. 3737-3748 (December 2017)
Permanent URL http://hdl.handle.net/2078.1/189175
  1. Global Health Observatory (GHO) (2013). http://www.who.int/gho/alcohol/harms_consequences/deaths_alcohol/en/
  2. Rangaswamy Madhavi, Jones Kevin A., Porjesz Bernice, Chorlian David B., Padmanabhapillai Ajayan, Kamarajan Chella, Kuperman Samuel, Rohrbaugh John, O'Connor Sean J., Bauer Lance O., Schuckit Marc A., Begleiter Henri, Delta and theta oscillations as risk markers in adolescent offspring of alcoholics, 10.1016/j.ijpsycho.2006.10.003
  3. Begleiter Henri, Porjesz Bernice, Genetics of human brain oscillations, 10.1016/j.ijpsycho.2005.12.013
  4. Porjesz Bernice, Rangaswamy Madhavi, Kamarajan Chella, Jones Kevin A., Padmanabhapillai Ajayan, Begleiter Henri, The utility of neurophysiological markers in the study of alcoholism, 10.1016/j.clinph.2004.12.016
  5. Kamarajan Chella, Porjesz Bernice, Jones Kevin A., Chorlian David B., Padmanabhapillai Ajayan, Rangaswamy Madhavi, Stimus Arthur T., Begleiter Henri, Spatial-anatomical mapping of NoGo-P3 in the offspring of alcoholics: evidence of cognitive and neural disinhibition as a risk for alcoholism, 10.1016/j.clinph.2004.12.015
  6. Rangaswamy Madhavi, Porjesz Bernice, Ardekani Babak A., Choi Steven J., Tanabe Jody L., Lim Kelvin O., Begleiter Henri, A functional MRI study of visual oddball: evidence for frontoparietal dysfunction in subjects at risk for alcoholism, 10.1016/j.neuroimage.2003.09.018
  7. Hada Michio, Porjesz Bernice, Chorlian David B, Begleiter Henri, Polich John, Auditory P3a deficits in male subjects at high risk for alcoholism, 10.1016/s0006-3223(00)01049-0
  8. Porjesz B., Begleiter H., Event-related potentials in individuals at risk for alcoholism, 10.1016/0741-8329(90)90033-9
  9. Chen Andrew C., Rangaswamy Madhavi, Porjesz Bernice, Endophenotypes in psychiatric genetics, Principles of Psychiatric Genetics ISBN:9781139025997 p.347-362, 10.1017/cbo9781139025997.030
  10. Jones Kevin A., Porjesz Bernice, Chorlian David, Rangaswamy Madhavi, Kamarajan Chella, Padmanabhapillai Ajayan, Stimus Arthur, Begleiter Henri, S-transform time-frequency analysis of P300 reveals deficits in individuals diagnosed with alcoholism, 10.1016/j.clinph.2006.02.028
  11. Kamarajan Chella, Rangaswamy Madhavi, Manz Niklas, Chorlian David B., Pandey Ashwini K., Roopesh Bangalore N., Porjesz Bernice, Topography, power, and current source density of theta oscillations during reward processing as markers for alcohol dependence, 10.1002/hbm.21267
  12. Liu Yi-Hung, Wu Chien-Te, Cheng Wei-Teng, Hsiao Yu-Tsung, Chen Po-Ming, Teng Jyh-Tong, Emotion Recognition from Single-Trial EEG Based on Kernel Fisher’s Emotion Pattern and Imbalanced Quasiconformal Kernel Support Vector Machine, 10.3390/s140813361
  13. Yuvaraj R., Murugappan M., Ibrahim Norlinah Mohamed, Omar Mohd Iqbal, Sundaraj Kenneth, Mohamad Khairiyah, Palaniappan R., Satiyan M., Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study, 10.1142/s021963521450006x
  14. Yamaguchi C., Fourier and wavelet analyses of normal and epileptic electroencephalogram (EEG), 10.1109/cne.2003.1196847
  15. Akin M., 10.1023/a:1015075101937
  16. Natarajan R, Samraj A (2014) Classification performance of new fusion features of P300 in visual evoked potentials from EEG to distinguish alcoholics and controls. Aust J Basic Appl Sci 8(9):52–63
  17. Sun Shiliang, Zhou Jin, A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces, 10.1109/ijcnn.2014.6889525
  18. Murugappan Murugappan, Murugappan Subbulakshmi, Zheng Bong Siao, Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT), 10.1589/jpts.25.753
  19. Omerhodzic I, Avdakovic S, Nuhanovic A, Dizdarevic K (2010) Energy distribution of EEG signals: EEG signal wavelet-neural network classifier. World Acad Sci Eng Technol 61:1190–1195
  20. Ingber L (2014) Database. http://kdd.ics.uci.edu/databases/eeg/eeg.html
  21. Daubechies Ingrid, Orthonormal bases of compactly supported wavelets, 10.1002/cpa.3160410705
  22. Mallat S.G., A theory for multiresolution signal decomposition: the wavelet representation, 10.1109/34.192463
  23. Kozakevicius Alice, Schmidt Alex A., Wavelet transform with special boundary treatment for 1D data, 10.1007/s40314-013-0050-6
  24. DONOHO DAVID L., JOHNSTONE JAIN M., Ideal spatial adaptation by wavelet shrinkage, 10.1093/biomet/81.3.425
  25. Bayer FM, Kozakevicius A (2010) SPC-Threshold: Uma Proposta de Limiarização para Filtragem Adaptativa de Sinais. Trend Math Appl Comput 11(2):121–132. doi: 10.105540/tema.2010.011.02.0121
  26. Haykin S (1998) Neural networks: a comprehensive foundation. Prentice Hall PTR, Upper Saddle River. ISBN 0132733501
  27. Tsoi AC, So DSC, Sergejew AA (1993) Classification of electroencephalogram using artificial neural networks. In: 7th NIPS conference on advances in neural information processing systems 6, Denver, Colorado, USA
  28. Abry P (1997) Ondelettes et Turbulences. Multirésolutions, Algorithmes de Décomposition, Invariance d’Échelle et Signaux de Pression, Nouveaux Essais, Diderot, Paris
  29. Lopes C.D., Mainardi J.O., Zaro M.A., Susin A.A., Classification of event-related potentials in individuals at risk for alcoholism using wavelet transform and artificial neural network, 10.1109/cibcb.2004.1393943
  30. Ting Wu, Guo-zheng Yan, Bang-hua Yang, Hong Sun, EEG feature extraction based on wavelet packet decomposition for brain computer interface, 10.1016/j.measurement.2007.07.007