An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG

This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.PMID:37246419 | DOI:10.1177/15500594231178274

Source: Clinical EEG and Neuroscience - May 29, 2023 Category: Neuroscience Authors: R Menaka R Karthik S Saranya M Niranjan S Kabilan Source Type: research