Journal of Scientific and Industrial Research

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Recurrence Quantification Analysis of EEG signals for Children with ASD


Affiliations
1 Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai 600 127, India
2 Department of Electronics Engineering, Vellore Institute of Technology, Chennai 600 127, India
3 Department of Speech Language and Hearing Sciences, Sri Ramachandra Institute of Higher Education and Research, Chennai 600 116, India

The present study aims at identifying the brain response for auditory/visual stimuli in typically developing (TD) and children with autism through electroencephalography (EEG). Early diagnoses do help in customized training and progressing the children in regular stream. To reveal the underlying brain dynamics, non-linear analysis was employed. In the current study, Recurrent Quantification Analysis (RQA) with varying parameters was analyzed. For better information retrieval, cosine distance metric is additionally considered for analysis and compared with other distance metrics in RQA. Each computational combination of RQA is measured and the responding channels were analyzed and discussed. It was observed that the FAN neighborhood with cosine distance parameters was able to discriminate between ASD and TD prominently.
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  • Recurrence Quantification Analysis of EEG signals for Children with ASD

Abstract Views: 280  | 

Authors

R Menaka
Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai 600 127, India
M Thanga Aarthy
Department of Electronics Engineering, Vellore Institute of Technology, Chennai 600 127, India
Renuka Mahadev Chavan
Department of Electronics Engineering, Vellore Institute of Technology, Chennai 600 127, India
R C Perumal
Department of Speech Language and Hearing Sciences, Sri Ramachandra Institute of Higher Education and Research, Chennai 600 116, India
Mahima S Menon
Department of Speech Language and Hearing Sciences, Sri Ramachandra Institute of Higher Education and Research, Chennai 600 116, India

Abstract


The present study aims at identifying the brain response for auditory/visual stimuli in typically developing (TD) and children with autism through electroencephalography (EEG). Early diagnoses do help in customized training and progressing the children in regular stream. To reveal the underlying brain dynamics, non-linear analysis was employed. In the current study, Recurrent Quantification Analysis (RQA) with varying parameters was analyzed. For better information retrieval, cosine distance metric is additionally considered for analysis and compared with other distance metrics in RQA. Each computational combination of RQA is measured and the responding channels were analyzed and discussed. It was observed that the FAN neighborhood with cosine distance parameters was able to discriminate between ASD and TD prominently.