Comparing Maximally Collapsing Metric Learning and Optimized Tunable Q-Wavelet Transform for Schizophrenia Identification Using EEG Signals.

Electroencephalography (EEG) is a noninvasive technique for recording brain electrical activity and supporting the assessment of psychiatric disorders. This study proposes a framework for distinguishing individuals with Schizophrenia (SZ) from healthy subjects using EEG data. The approach employs a transformer based on Maximally Collapsing Metric Learning (MCML) applied to feature-vector matrices and compares it with optimized Tunable Q-Wavelet Transform (TQWT) decomposition. Eight nonlinear features are extracted from both approaches and evaluated using *k*-Nearest Neighbor (*k*-NN), Optimized Decision Tree (ODT), and Deep Neural Network (DNN) classifiers. Results achieve accuracies of 100%, 99.94%, and 99.91%, respectively, using one electrode channel.