Sensory Profile for Autistic Children by Using EEG Biosignal
DOI:
https://doi.org/10.61841/qjajrp16Keywords:
Electroencephalography (EEG), autistic children, neurological, sensory profileAbstract
The neural activities in a brain can be measured in terms of voltages by Electroencephalography (EEG). The neurological problems are diagnosed by EEG because it is capable of being recorded over longer period of time and it is non-invasive. The children suffering from ASD are unable to express their emotions because of lack of proper processing of information in brain. This work is focussed towards building a sensory profile that differentiates among different types of sensory responses by using an EEG biosignal potential. Different states of emotion are identified by EEG signals such as super learning, positive thinking and relaxation of light and lie in between range of 8-12 Hz. This research involved participation of 64 children among which 34 were suffering from ASD whereas 30 were normal. While recoding EEG data, children were provided with visual, vestibular, taste, sound and vestibular sensory simulations. The EEGLAB software and wavelet transform wa used for filtering raw EEG data by using “independent component analysis (ICA)”. Later on, sensory profile was built by approximating entropy, standard deviations and means after extracting it from filtered EEG signals
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References
[1] C. L. Hilton et al., “Sensory responsiveness as a predictor of social severity in children with high functioning
autism spectrum disorders,” J. Autism Dev. Disord., 2010.
[2] T. Nakamura, S. Takahashi, and J. Tanaka, Human-machine interface apparatus. 2010.
[3] J. Ermer and W. Dunn, “The Sensory Profile: A Discriminant Analysis of Children with and Without
Disabilities,” Am. J. Occup. Ther., 1998.
[4] N. B. Brown and W. Dunn, “Relationship between context and sensory processing in children with autism,”
Am. J. Occup. Ther., 2010.
[5] S. D. Tomchek, R. A. Huebner, and W. Dunn, “Patterns of sensory processing in children with an autism
spectrum disorder,” Res. Autism Spectr. Disord., vol. 8, no. 9, pp. 1214–1224, 2014.
[6] P. Bach-y-Rita and S. W. Kercel, “Sensory substitution and the human-machine interface,” Trends in
Cognitive Sciences. 2003.
[7] M. Hochhauser and B. Engel-Yeger, “Sensory processing abilities and their relation to participation in leisure
activities among children with high-functioning autism spectrum disorder (HFASD),” Res. Autism Spectr.
Disord., 2010.
[8] M. Nandish, S. Michahial, K. Hemanth, and F. Ahmed, “Feature Extraction and Classification of EEG Signal
Using Neural Network Based Techniques,” Int. J. Eng. Innov. Technol., 2012.
[9] D. P. Subha, P. K. Joseph, R. Acharya U, and C. M. Lim, “EEG signal analysis: a survey.,” J. Med. Syst.,
2010.
[10] A. Zabidi, W. Mansor, Y. K. Lee, and C. W. N. F. Che Wan Fadzal, “Short-time Fourier Transform analysis of
EEG signal generated during imagined writing,” in Proceedings of the 2012 International Conference on
System Engineering and Technology, ICSET 2012, 2012.
[11] J. A. Hofheimer, S. J. Sheinkopf, and L. T. Eyler, “Autism risk in very preterm infants - New answers, more
questions,” Journal of Pediatrics. 2014.
[12] A. Procházka, J. Kukal, and O. Vyšata, “Wavelet transform use for feature extraction and EEG signal segments
classification,” in 2008 3rd International Symposium on Communications, Control, and Signal Processing,
ISCCSP 2008, 2008.
[13] R. Panda, P. S. Khobragade, P. D. Jambhule, S. N. Jengthe, P. R. Pal, and T. K. Gandhi, “Classification of
EEG signal using wavelet transform and support vector machine for epileptic seizure diction,” in
International Conference on Systems in Medicine and Biology, ICSMB 2010 - Proceedings, 2010.
[14] T. T. Erguzel, S. Ozekes, O. Tan, and S. Gultekin, “Feature Selection and Classification of
Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach,”
Clin. EEG Neurosci., 2015.
[15] C. W. N. F. C. Wan Fadzal, W. Mansor, L. Y. Khuan, and A. Zabidi, “Short-time fourier transform analysis of
EEG signal from writing,” in Proceedings - 2012 IEEE 8th International Colloquium on Signal Processing
and Its Applications, CSPA 2012, 2012
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