M. Muliyadi

Temporal Deep Learning with Multiscale Principal Component Features for Autism Classification from Electroencephalographic Signals

A comparative temporal deep learning framework for EEG-based ASD classification by evaluating principal component analysis (PCA) and multiscale principal component analysis (MS-PCA) as feature representations combined with a recurrent neural network with bidirectional long short-term memory (RNN-BiLSTM) and a temporal convolutional network with self-attention (TCN-SA).