Seyed Kamaledin Setarehdan

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.

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

This study uses fNIRS and HRV data to classify sustained attention states, showing that combined data improve classification accuracy.

This study shows saccadic eye movements in macaques are phase-locked to beta LFP oscillations in the FEF, with alignment depending on target location relative to neuronal response fields.

The paper introduces a wavelet-based adaptive algorithm to improve hemodynamic response estimation in fNIRS by incorporating protocol constraints and Kalman filtering.