EEG-Based Analysis of Hemispheric Lateralisation for Autism Screening Using Machine Learning †

Artikel i vetenskaplig tidskrift, 2026

Autism spectrum disorder (ASD) is a neurodevelopmental condition where early detection is crucial for improving outcomes. Electroencephalography (EEG) offers a non-invasive approach for identifying potential biomarkers. In this study, we investigate hemispheric asymmetry using the Lateralisation Index (LI) derived from EEG signals and evaluate its effectiveness for ASD classification. Using a small dataset of children with and without ASD, we applied several machine learning models, including Logistic Regression, Support Vector Machines and Random Forest. Particular attention was given to evaluation strategies to avoid overfitting and data leakage. While initial results suggested moderate classification performance, repeated validation indicated unstable generalisation. Our findings highlight both the potential and limitations of LI-based features in small-sample settings and emphasise the importance of robust evaluation in EEG-based machine learning studies.