What question did this study set out to answer?

To develop a deep feature fusion network that incorporates spatial structure for better classification of autism spectrum disorder from EEG signals.

April 16, 2026Scientific Reports0 citationsOpen Access

Spatial structure based deep feature fusion network for autism spectrum disorder classification

KSK. S. Shwetha GDG. Deepak SCS Chaitra

Key Points

To develop a deep feature fusion network that incorporates spatial structure for better classification of autism spectrum disorder from EEG signals.
Utilized a spatial structure based feature fusion network (SSFFN) to analyze EEG signals.
Integrated spatial proximity and functional connectivity to capture interactions between brain regions.
Applied long short term memory (LSTM) predictor to consider long-term temporal dependencies in data.
Conducted experiments using Sheffield and KAU datasets for effectiveness validation.
Achieved 1% higher accuracy in ASD classification compared to existing time frequency synergy networks.
Obtained 4.4% higher recall rate on the same datasets, indicating improved identification of ASD cases.

Abstract

Autism spectrum disorder (ASD) is a neuro-developmental disorder that has serious impact on person’s social interaction, communication, and behavior. Non intrusive detection through analysis of electroencephalogram (EEG) signals with deep learning techniques has been the prominent area of research in ASD diagnosis. The current deep learning techniques processed EEG signals without considering their spatial structure. They treat EEG features as independent or grid-like sequences, ignoring the non-Euclidean structure of brain connectivity. As the result, the features do not capture the intricate relationship between signals obtained from multiple electrodes considering both spatial and long term temporal dependencies. To solve this problem, this work proposes a spatial structure based feature fusion network (SSFFN) which considers two attributes of spatial proximity and functional connectivity to capture local and long range interactions between brain regions as fused features. These fused features are used for ASD classification using long short term (LSTM) predictor considering the long term temporal dependencies. Experimental analysis of proposed solution against Sheffield dataset and KAU dataset showed a 1% higher accuracy and 4.4% higher recall compared to most recent time frequency synergy networks.

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