EEG and AI Fusion for Personalized VR-Based Learning, An Experimental Study for Children With Autism Spectrum Disorder Using Emotiv Insight
DOI:
https://doi.org/10.22399/ijcesen.4611Keywords:
Emotiv, Autism Spectrum Disorder, EEG signals, Neuroscience, Machine Learning, AI FusionAbstract
This Research proposes experimental evaluation data on the use of Emotiv 5-channel medical tech-based equipment for the analysis of hybrid electroencephalography (EEG) sensory signals, for Autism Spectrum Disorder (ASD) individuals to diagnose their behavioral patterns, cognitive assessment, integrating neuroscience, Artificial Intelligence (AI), and immersive Virtual Reality (VR) learning environments to enhance educational experiences. The neuroadaptive learning approaches emerged as a good solution, it adapt instructional methodology strategies based on the learners ' needs with effective personalized solutions. This research sheds light on addressing the gap by presenting a context that can combine technological diagnosis and learning solutions for ASD learners. The experimental study involved around ten individual children with ASD, aged between 8 and 14 years old, from a special ASD education center. The tech device using the Emotiv Insight wireless headset with five electrodes positioned at AF3, F3, F4, AF4, and Pz fixed on the head for capturing the EEG brain signals was recorded. These five electrodes were mounted on the prefrontal and parietal brain regions to identify the cognitive, emotional, and attention behavior patterns, variations that influence their learning performance in individuals with ASD.
The study involves a machine learning systematic preprocessing analysis approach on EEG data for data filtering, correction, component analysis, noise removal, etc. Features extraction, like alpha power, theta beta ratio, stress-related marker, attention, and engagement, was applied to generate an adaptive decision on behavioral guidance and structured instruction. For an ASD individual, during EEG monitoring assessment sessions, users were enabled with 3D objects with rich, visually engaging tools, and learning content was adapted with personalized gamified cognitive tasks to simulate social interactions, reduce stress, and feel more relaxed during the instructional flow. The study involved pre and post evaluations on data using Emotiv EEG with a sensory calibration to capture the signal for qualitative behavioral cognitive assessments and to guide personalized education using an EEG-AI- VR integrated system for ASD learners.
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