This paper develops an automated schizophrenia diagnosis method using EEG signals by extracting time, frequency, and time-frequency features and achieving 100% accuracy with three classifiers.

DOI 10.64898/2026.04.13.718103

electronic health recordsmachine learning (classical)

𝕏 PostBluesky

NeurosciencebioRxivPreprintApr 10, 2026

EEG-based Schizophrenia Detection Using Spectral, Entropy, and Graph Connectivity Features with Machine Learning

Nazila Ahmadi Daryakenari, Seyed Kamaledin Setarehdan

This study uses EEG features and machine learning to detect schizophrenia, achieving high accuracy with Random Forest, but notes the need for larger validation.

DOI 10.64898/2026.04.08.717137

electronic health recordsmachine learning (classical)

𝕏 PostBluesky