Quantitative assessment of cognitive profile and brain asymmetry in the characterization of autism spectrum in children: A task-based EEG study

Author:

Balathay Divya1ORCID,Narasimhan Udayakumar2,Belo David3,Anandan Kavitha1

Affiliation:

1. Centre for Healthcare Technologies, Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India

2. Department of Pediatrics, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu, India

3. Machine Learning for Time Series at Fraunhofer Portugal AICOS, Seixal, Setubal, Portugal

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children. Assessment of EEG signals acquired during specific cognitive tasks is more appropriate in identifying brain functional and cognitive load variations. The spectral power of EEG sub-band frequency and parameters related to brain asymmetry has the potential to be employed as indices to characterize brain functioning. Thus, the objective of this work is to analyze the cognitive task-based electrophysiological variations in autistic and control groups, using EEG acquired during two well-defined protocols. Theta to Alpha ratio (TAR) and Theta to Beta ratio (TBR) of absolute powers of the respective sub-band frequencies have been estimated to quantify the cognitive load. The variations in interhemispheric cortical power measured by EEG were studied using the brain asymmetry index. For the arithmetic task, the TBR of the LF group was found to be considerably higher than the HF group. The findings reveal that the spectral powers of EEG sub-bands can be a key indicator in the assessment of high and low-functioning ASD to facilitate appropriate training strategies. Instead of depending solely on behavioral tests to diagnose autism, it could be a beneficial approach to use task-based EEG characteristics to differentiate between the LF and HF groups.

Publisher

SAGE Publications

Subject

Mechanical Engineering,General Medicine

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