A Multi-Modal Dataset of EEG Signals and M-CHAT Assessments for Autism Spectrum Disorder Detection

This dataset integrates electroencephalogram (EEG) recordings with behavioral screening outcomes from the Modified Checklist for Autism in Toddlers (M-CHAT), providing a multi-modal resource for studying early detection of Autism Spectrum Disorder (ASD). The dataset includes data from 28 children — 15 diagnosed with ASD and 13 typically developing controls — all assessed using the same experimental and behavioral protocols.
EEG signals were recorded using the NeuroCONCISE FlexEEG 8-channel system following the international 10–20 electrode placement scheme. From the EEG data, four statistical features were extracted for each subject: skewness, kurtosis, coefficient of variation, and entropy, representing distributional, variability, and complexity-based signal characteristics.
In parallel, M-CHAT scores were collected for each participant to provide behavioral assessment measures. The dataset also includes benchmark classification results obtained using multiple machine-learning algorithms (SVM, Neural Network, Logistic Regression, Random Forest, XGBoost) for reference and reproducibility.
This resource enables researchers to explore neurophysiological and behavioral correlates of ASD, test feature-fusion approaches, and benchmark novel detection algorithms.