Improved EEG-Based Emotion Classification via Stockwell Entropy and CSP Integration

Entropy, Journal Year: 2025, Volume and Issue: 27(5), P. 457 - 457

Published: April 24, 2025

Traditional entropy-based learning methods primarily extract the relevant entropy measures directly from EEG signals using sliding time windows. This study applies differential to a time-frequency domain that is decomposed by Stockwell transform, proposing novel emotion recognition method combining and common spatial pattern (CSP). The results demonstrate effectively captures features of high-frequency signals, CSP-transformed show superior discriminative capability for different emotional states. experimental indicate proposed achieves excellent classification performance in Gamma band (30–46 Hz) recognition. combined approach yields high accuracy binary tasks (“positive vs. neutral”, “negative “positive negative”) maintains satisfactory three-class task negative neutral”).

Language: Английский

A Systematic Review of Artificial Intelligence Techniques Based on Electroencephalography Analysis in the Diagnosis of Epilepsy Disorders: A Clinical Perspective

Epilepsy Research, Journal Year: 2025, Volume and Issue: 215, P. 107582 - 107582

Published: May 16, 2025

Language: Английский