Graph signal processing and graph learning approaches to Schizophrenia pattern identification in brain Electroencephalogram

The detection of Schizophrenia (SZ) directly from brain Electroencephalogram (EEG) signals has recently gained importance. Traditionally, EEG-based SZ detection is done by either manually extracting features from each EEG channel and using Machine Learning classifiers or by Deep Learning-based automated detection. These methods neglect the functional interactions between spatially distributed brain regions. This study proposes a graph signal processing and graph learning framework to detect SZ. Here, the individual channels’ features and the global interactions are combined into a unified graph signal (GS) representation. The proposed representation consists of the GS values and the underlying connectivity network. An SZ-specific feature, namely, the permutation entropies of each electrode’s signal, is considered as the GS values. The graph learning method learns the underlying network from a collection of GS observations. The learned network serves as the Fourier basis for the graph Fourier and wavelet transform . Utilizing these transforms, the graph spectral features are extracted from GSs. Lastly, the extracted features are classified using a set of Machine Learning models. The proposed graph signal processing and graph learning method is an effective approach to decode Schizophrenia patterns in brain EEG signals and outperform the existing approaches. • Automated detection of Schizophrenia using Electroencephalogram brain signals. • Obtaining the functional connectivity network using graph learning technique. • Graph signal representation using the learned graph and permutation entropy. • Graph Fourier and graph wavelet transforms for spectral feature extraction.