Shannon entropy measures for EEG signals in tinnitus

Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study

In the analysis of electroencephalography (EEG), entropy can be used to quantify the rate of generation of new information. Entropy has long been known to suffer from variance that arises from its calculation. From a sensor's perspective, calculation of entropy from a period of EEG recording can be treated as physical measurement, which suffers from measurement noise. We showed the feasibility of using Kalman filtering to reduce the variance of entropy for simulated signals as well as real-world EEG recordings. In addition, we also manifested that Kalman filtering was less time-consuming than moving average, and had better performance than moving average and exponentially weighted moving average. In conclusion, we have treated entropy as a physical measure and successfully applied the conventional Kalman filtering with fixed hyperparameters. Kalman filtering is expected to be used to reduce measurement noise when continuous entropy estimation (for example anaesthesia monitoring) is essential with high accuracy and low time-consumption.

A multi-head residual connection GCN for EEG emotion recognition

Electroencephalography (EEG) emotion recognition is a crucial aspect of human-computer interaction. However, conventional neural networks have limitations in extracting profound EEG emotional features. This paper introduces a novel multi-head residual graph convolutional neural network (MRGCN) model that incorporates complex brain networks and graph convolution networks. The decomposition of multi-band differential entropy (DE) features exposes the temporal intricacy of emotion-linked brain activity, and the combination of short and long-distance brain networks can explore complex topological characteristics. Moreover, the residual-based architecture not only enhances performance but also augments classification stability across subjects. The visualization of brain network connectivity offers a practical technique for investigating emotional regulation mechanisms. The MRGCN model exhibits average classification accuracies of 95.8% and 98.9% for the DEAP and SEED datasets, respectively, highlighting its excellent performance and robustness.

Use of Some Relevant Parameters for Primary Prediction of Brain Activity in Idiopathic Tinnitus Based on a Machine Learning Application

INTRODUCTION

Tinnitus is one of the most common complaints, distressing about 15-24% of the adult population. Because of its pathophysiology heterogeneity, no curable treatment has been attained yet. Even though a neuromodulation management technique based on the tinnitus network model is currently being developed, it has not yet worked because the most involved brain areas still remain unpredictable from the patient's individual clinical and functional profile. A remarkable correlation between tinnitus network activity and the subjective measures of tinnitus like perceived loudness and annoyance and functional handicap is well established. Therefore, this study aimed to develop software for predicting the involved brain areas in the tinnitus network based on the subjective characteristics and clinical profile of patients using a supervised machine-learning method.

METHODS

The involved brain areas of 30 tinnitus patients ranging from 6 to 80 months in duration were recognized by using QEEG and sLORETA software. There was a correlation between subjective information and those areas of activities in all rhythms by which we wrote our software.

RESULTS

For verification and validation of the software, we compared and analyzed the results with SPSS data and the receiver operating characteristic (ROC) curves.

CONCLUSIONS

The findings of this study confirmed the effectiveness of the software in predicting the brain activity in tinnitus subjects; however, some other important parameters can be added to the model to strengthen its reliability and feasibility in clinical use.

Study on tinnitus-related electroencephalogram microstates in patients with vestibular schwannomas

Tinnitus is closely associated with cognition functioning. In order to clarify the central reorganization of tinnitus in patients with vestibular schwannoma (VS), this study explored the aberrant dynamics of electroencephalogram (EEG) microstates and their correlations with tinnitus features in VS patients. Clinical and EEG data were collected from 98 VS patients, including 76 with tinnitus and 22 without tinnitus. Microstates were clustered into four categories. Our EEG microstate analysis revealed that VS patients with tinnitus exhibited an increased frequency of microstate C compared to those without tinnitus. Furthermore, correlation analysis demonstrated that the Tinnitus Handicap Inventory (THI) score was negatively associated with the duration of microstate A and positively associated with the frequency of microstate C. These findings suggest that the time series and syntax characteristics of EEG microstates differ significantly between VS patients with and without tinnitus, potentially reflecting abnormal allocation of neural resources and transition of functional brain activity. Our results provide a foundation for developing diverse treatments for tinnitus in VS patients.

EEG signal classification of tinnitus based on SVM and sample entropy

The prevalence of tinnitus is high and seriously affects the daily life of patients. As the pathogenesis of tinnitus is not yet clear, there is a lack of rapid and objective diagnostic modalities. In order to provide clinicians with an objective diagnostic approach, this paper combines time-frequency domain and non-linear power analysis to investigate the differences in the specificity of the EEG signal in tinnitus patients compared to healthy subjects. In this paper, resting-state electroencephalograms (EEG) were collected from 10 cases each of tinnitus patients and healthy subjects, and the data from the two groups were compared in the δ (0.5 - 3 .5 Hz), θ (4 - 7.5 Hz), α1 (8 - 10 Hz), α2 (10 - 12 Hz), β1 (13 - 18 Hz), β2 (18.5 - 21 Hz), β3 (21.5 - 30 Hz), and γ (30.5 - 44 Hz) bands for the differences in sample entropy values. The results of the resting state experiment revealed that the δ, α2 and β1 band samples of tinnitus patients all had greater entropy values than healthy subjects, with extremely significant differences compared to healthy subjects (p < 0.01). It is mainly concentrated in the δ band in the right parietal region of the cerebral cortex, the α2 band in the central region, and the γ band in the left prefrontal region. Finally, support vector machines combined with optimal feature combinations were used to achieve objective recognition of tinnitus disorders, with an 8.58% increase in accuracy compared to other features. Through the above study, entropy reflects the degree of chaos in the brain and the chaotic characteristics of the resting state EEG signal can characterise the onset of tinnitus, the results of which can help clinicians in the early diagnosis of tinnitus.