Advanced Strategies for ASD Detection

This chapter presents a narrative review of methodologies that researchers use in multiple studies to achieve early diagnosis of Autism Spectrum Disorder (ASD) with electroencephalographic (EEG) signals and machine learning. EEG emerges as a promising tool to identify patterns linked to ASD. Researchers combine it with advanced preprocessing techniques and machine learning algorithms. The text first highlights brain patterns revealed by EEG. Then it describes preprocessing approaches, such as band-pass filtering, windowing, the Fourier Transform, and Independent Component Analysis (ICA). Additional techniques, such as the Short-Time Fourier Transform (STFT) and wavelet transforms, capture temporal and frequency features. The chapter then explores classifiers, including neural networks and support vector machines (SVM). Researchers select these methods based on data characteristics and study goals. This choice aims to optimize diagnostic accuracy. Combining different approaches strengthens model robustness. The final section addresses the limitations of these methods. These constraints involve reliance on multimodal data, high computational complexity, and the specificity of frequency bands and EEG channels. Despite these challenges, advancements in EEG and machine learning for ASD diagnosis offer immense potential. They can reshape diagnostics and improve the quality of life for affected individuals.