Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques

This study aims to deepen the understanding and classification of tinnitus through a comprehensive analysis of EEG signals utilizing innovative microstate analysis techniques and cutting-edge machine learning approaches. EEG data were collected from two datasets: a primary dataset with 36 participants (16 healthy, 20 tinnitus) and a public dataset with 37 participants (15 healthy, 22 tinnitus). Signals were decomposed into five frequency bands (delta, theta, alpha, beta, gamma) using Daubechies 4 wavelet at five decomposition levels. Microstate features (Duration, Occurrence, Mean Global Field Power, and Coverage) were extracted across four microstate configurations (4-state to 7-state) under both eyes-closed and eyes-open conditions. Classification was performed using SVM, Decision Tree, Random Forest, and Deep Neural Networks. Additionally, pre-trained models (VGG16, ResNet50, Xception) were used with a novel feature-to-image transformation approach for validation. Analysis revealed significant alterations in beta band microstates, with microstate A showing increased duration (+ 7.8% to + 11.2%) and microstate B showing decreased duration (- 9.0% to - 13.8%) in tinnitus patients. Occurrence rates were markedly elevated (~ 28-29% higher) in the tinnitus group. Transition probability analysis identified distinctive patterns between groups, with the most pronounced differences observed in gamma band (6-state configuration) during eyes-closed condition (healthy: F → B = 0.143; tinnitus: C → D = 0.153) and beta band (7-state configuration) also during eyes-closed condition (healthy: E → A = 0.091; tinnitus: C → E = 0.082). In the eyes-open condition, gamma band with 7 microstates showed substantial differences in transition patterns (healthy: E → A = 0.149; tinnitus: C → G = 0.157). Classification performance was exceptional, with DNN achieving 100% accuracy in the gamma frequency band during eyes-open condition with 5-state configuration. Frequency band analysis demonstrated that gamma band performed best for open eyes (99.89% accuracy) and beta band excelled for closed eyes (96.46% accuracy). Validation with pre-trained models showed ResNet50 with SVM classifier using 6-state configurations provided optimal discrimination (100% accuracy). EEG microstate dynamics in beta and gamma bands serve as reliable markers for distinguishing tinnitus patients. These findings provide insights into tinnitus-related neural alterations and highlight microstate analysis as a potential objective diagnostic tool for guiding personalized neuromodulation therapies.