Improving Autism Detection Accuracy with an Optimized Local-Asymmetric Adaptive Hybrid GCN for EEG Data
DOI:
https://doi.org/10.56294/dm20261339Keywords:
Autism spectrum disorder, Resting-state EEG, Task-based EEG, DEBCWGAN, GCN, Quantum mechanics, Artificial gorilla troops optimizerAbstract
Autism spectrum disorder (ASD) is an intricate nervous disorder typically diagnosed through the use of electroencephalography (EEG). A novel model named Dual Encoder-Balanced Conditional Wasserstein Generative Adversarial Network with Resting-state EEG-based Hybrid Graph Convolutional Network (DEBCWGAN-Rest-HGCN) was made from this context. By fixing the class imbalance and making synthetic EEG samples, it was able to detect ASD with encouraging results. However, it ignores the dynamic brain patterns recorded by task-based EEG in favor of resting-state EEG. The Rest-HGCN model also cannot successfully capture the uneven spatial and temporal aspects of EEG signals, and its fixed hyperparameters might make it less accurate in detecting different types of EEG data. This article presents a new model for finding and diagnosing ASD called the Optimized Local-Asymmetric Adaptive Hybrid GCN (OLA2HGCN). This model uses both spatial and temporal information from resting-state and task-driven EEG signals. It is based on the way autism affects brain connections and a variation amid the left and right hemispheres. The LA2HGCN can efficiently collect discrete spatiotemporal EEG information through distinct areas and hemispheres by improving the HGCN model with hierarchical feature extraction and fusion approaches. This model has a time based feature extraction approach in the cognitive prior graph branch that picks up temporal characteristics inside and between brain areas. It also has an adaptive GCN for spatial feature extraction across non-Euclidean distributions of electrodes. An attention layer shows how each hemisphere helps with ASD classification. A new Quantum Artificial Gorilla Troops Optimizer (QGTO) is also presented to help the LA2HGCN model choose the best hyperparameters. The QGTO is based on the social intelligence of gorilla tribes. It rapidly traverses intricate search spaces and achieves an equilibrium between exploration and exploitation. By adding quantum mechanics to the GTO method, it can better find its way through complicated search spaces and stay away from local optima. This makes hyperparameter selection more successful. Finally, the test results show that the DEBCWGAN- OLA2HGCN on the EEG Dataset for ASD and the ABC-CT dataset are 95.04% and 92.27% accurate, respectively, when compared to other algorithms.
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