International Journal of Image, Graphics and Signal Processing(IJIGSP)
ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)
Published By: MECS Press
IJIGSP Vol.4, No.3, Apr. 2012
EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis
Full Text (PDF, 244KB), PP.35-41
Diagnosis of autism is one of the difficult problems facing researchers. To reveal the discriminative pattern between autistic and normal children via electroencephalogram (EEG) analysis is a big challenge. The feature extraction is averaged Fast Fourier Transform (FFT) with the Regulated Fisher Linear Discriminant (RFLD) classifier.
Gaussinaty condition for the optimality of Regulated Fisher Linear Discriminant (RFLD) has been achieved by a well-conditioned appropriate preprocessing of the data, as well as optimal shrinkage technique for the Lambda parameter. Winsorised Filtered Data gave the best result.
Cite This Paper
Mahmoud I. Kamel , Mohammed J. Alhaddad, Hussein M. Malibary, Khalid Thabit, Foud Dahlwi, Ebtehal A. Alsaggaf, Anas A. Hadi,"EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis", IJIGSP, vol.4, no.3, pp.35-41, 2012.
T. Fabricius, "The Savant Hypothesis: Is autism a signal-processing problem?," Medical Hypotheses,ScienceDirect, 2010.
H. BehnamA, A. SheikhaniB, M. R. MohammadiC, M. NoroozianD, and P. GolabiE, "Analyses of EEG background activity in Autism disorders with fast Fourier transform and short time Fourier measure," in International Conference on Intelligent and Advanced Systems 2007,IEEE paper 10368672 p1240 - 1244
Trottier G, Srivastava L, Walker CD. Etiology of infantile autism: a review of recent advances in genetic and neurobiological research. J Psychiatry Neurosci. 1999;24(2):103–115
Kai Velten "Mathematical Modeling and Simulation Introduction for Scientists and Engineers" 2009 WILEY-VCH Verlag GmbH & Co KGaA, Weinheim
S. A. S. E. Schipul , M. A. Just "Applying Machine Learning Techniques to Brain Imaging Characteristics to Distinguish Between Individuals with Autism and Neurotypical Controls " 2010.
C. A. N. Bosl, "Using EEGs to Diagnose Autism Spectrum Disorders in Infants: Machine-Learning System Finds Differences in Brain Connectivity," 2011.
L. M. Oberman, E. M. Hubbard, J. P. McCleery, E. L. Altschuler, V. S. Ramachandran, and J. A. Pineda, "EEG evidence for mirror neuron dysfunction in autism spectrum disorders," Cognitive Brain Research,ScienceDirect, vol. 24, pp. 190-198, 2005.
J. A. Pineda, D. Brang, E. Hecht, L. Edwards, S. Carey, M. Bacon, C. Futagaki, D. Suk, J. Tom, and C. Birnbaum, "Positive behavioral and electrophysiological changes following neurofeedback training in children with autism," Research in Autism Spectrum Disorders,ScienceDirect, vol. 2, pp. 557-581, 2008.
A. Sheikhani, H. Behnam, M. R. Mohammadi, M. Noroozian, and P. Golabi, "Connectivity analysis of quantitative Electroencephalogram background activity in Autism disorders with short time Fourier transform and Coherence values," 2008, pp. 207-212.
B. William, T. Adrienne, and N. Charles, "EEG complexity as a biomarker for autism spectrum disorder risk," BMC Medicine, vol. 9, 2011.
E. Milne, "Increased Intra-Participant Variability in Children with Autistic Spectrum Disorders: Evidence from Single-Trial Analysis of Evoked EEG," Frontiers in Psychology, vol. 2, 2011.
C. Croux, P. Filzmoser, and K. Joossens, "Classification efficiencies for robust linear discriminant analysis" Statistica Sinica, vol. 18, pp. 581-599, 2008.
G. Schalk and J. Mellinger, A Practical Guide to Brain-Computer Interfacing with BCI2000: Springer 2010.
Mahmoud I. Kamel, Mohammed Alhaddad, Hussein Malibary, Anas A. Hadi. "Improving P300 Speller by Common Average Reference (CAR)". To be published.
H. H. Monson, Statistical digital signal processing and modeling: John Wiley & Sons, 1996.
R.O. Duda, P.E. Hart, and D.G. Stork, Pattern classification, 2nd ed.Wiley, New York, (2001).
B. Blankertz et al. (eds.), Brain–Computer Interfaces, The Frontiers Collection, Springer-Verlag Berlin Heidelberg 2010
J.E. Vos, Representation in the frequency domain of non-stationary EEGs, G Dolce, H Künkel, Editors , Computerized EEG analysis, Gustav Fischer Verlag, Stuttgart (1975), pp. 41–50
Ulrich Hoffmann , Jean-Marc Vesin, Touradj Ebrahimi, Karin Diserens, "An efficient P300-based brain–computer interface for disabled subjects", Journal of Neuroscience Methods 167 (2008), pp. 115–125