IoT-enabled real-time EEG platform for early autism spectrum disorder diagnosis using deep learning

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder marked by persistent challenges in social interaction, communication, and behavioral patterns. The growing evidence shows that individuals with ASD have atypical brain connectivity, which makes electroencephalography (EEG) a valuable tool to study related neural activity. The non-invasive nature of EEG, its low cost, and its high temporal resolution have made it a focus for the early detection of ASD. However, most studies use an offline analysis, which limits their use in clinical practice. This paper introduces a real-time diagnostic platform that combines Internet of Things (IoT) technology and EEG to support the early identification of ASD using deep learning techniques.The framework integrates real-time EEG acquisition, signal preprocessing, deep learning-based classification models, and a Flask-based clinical interface. The clinical interface allows healthcare professionals to record EEG signals, perform real-time predictions, and generate diagnostic reports, whereas a secure guardian portal enables controlled access for patient monitoring. We evaluated time-series-based deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Units (GRU), Bidirectional Gated Recurrent Units (Bi-GRU), and hybrid architectures, using various data-splitting strategies and optimization algorithms. The CNN–BiGRU model trained with the Adam optimizer achieved the highest performance, with an F1-score of 96.40% and a Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) of 99.37%. These results show that deep learning models can effectively identify EEG patterns associated with ASD and enable reliable classification. The web-based platform also supports real-time monitoring and decision support, increasing its value in clinical settings.